=Paper=
{{Paper
|id=Vol-3303/paper7
|storemode=property
|title=A Neighbourhood-based Location- and Time-aware Recommender System
|pdfUrl=https://ceur-ws.org/Vol-3303/paper7.pdf
|volume=Vol-3303
|authors=Len Feremans,Robin Verachtert,Bart Goethals
|dblpUrl=https://dblp.org/rec/conf/recsys/FeremansVG22
}}
==A Neighbourhood-based Location- and Time-aware Recommender System==
https://ceur-ws.org/Vol-3303/paper7.pdf
A Neighbourhood-based Location- and Time-aware
Recommender System
Len Feremans1 , Robin Verachtert2 and Bart Goethals1
1
University of Antwerp, Belgium
2
Froomle N.V., Belgium
Abstract
We address the problem of location- and time-aware recommender systems where users with dynamically changing locations
are interested in trending and volatile items. Unlike existing work, we do not assume a known static location of each user
and derive user-locational preferences from their long-term history of implicit feedback. We propose a recommendation
model that accounts for spatial, temporal, popularity and social influences, thereby assuming items tagged with a location, i.e.
geotag, city or country. Key ingredients of our online method include: (1) deriving location preferences from the history,
(2) learning relevant nearby locations, (3) accounting for recency and popularity jointly, and (4) combining location- and
time-aware recommendations with collaborative filtering. Supported by realistic offline and online experiments on a large
dataset collected from a popular newspaper, and public datasets, we find that the proposed recommender outperforms
content-based and time-aware collaborative filtering approaches.
Keywords
Context-aware recommender systems, Location-based news recommendations, Collaborative filtering
1. Introduction sports event or a celebrity tweet. Finally, we have to
consider the relationship between locations, i.e. in some
Every day, users consume items tagged with locations, applications physical distances are more important (i.e.
e.g. local news articles from a particular city or Twitter point-of-interest recommendations in Facebook places),
tags trending in a specific region. Recommendations while in other applications (i.e. Twitter tags) location sim-
are essential to tackle information overload and filter ilarity is higher when many users consecutively prefer
relevant items from a huge set of available articles. In both locations [2].
this context, the first law of geography posed by Tobler In this work, we investigate the problem of location-
[25] is crucial: “everything is related to everything else, and time-aware recommender systems (LTARS) and rank-
but near things are more related than distant things”. ing highly volatile geotagged items by considering both
A common strategy for context-aware recommenda- spatio-temporal and user activity trends. We study fac-
tions [1] is pre-filtering, i.e. we collect the location of tors correlated with item relevance: (1) geographic fac-
each user and rank geotagged items nearby. However, tors, such as the geodesic distance between the geotag of
this strategy is problematic. Firstly, many users have an item and the inferred regional preferences of a user,
multiple and dynamic regions of preference, i.e. they and (2) user-item preference, i.e. the item-neighbourhood
might be interested in items near their home, work or based relevancy, and (3) the recency and popularity of
recent vacation stay. Secondly, even after filtering on a an item. This problem has been studied in the context
preferred location, there are some biases resulting from of time-aware recommendations [8, 3], location-aware
population density, i.e. items geotagged with a big city recommendations [5, 21, 24, 7, 19], context-aware rec-
will likely be more numerous and popular, which is likely ommendations [1, 9, 16] and location-based social net-
not relevant for all users near that city (vice versa, rural works [2, 28, 12]. A key difference with closely related
locations might lack recent item interactions). Addition- research by Pálovics et al. [21] is that we assume a user is
ally, we find that the intrinsic popularity bias is detrimen- interested in multiple locations and these locational pref-
tal to inferring regional preferences, i.e. an item might erences are unknown and dynamic. A second key differ-
be relevant to many users regardless of the associated ence we consider is the volatility of items, which is crucial
geotag such as a news item related to an international in certain domains such as news recommendations [15]
ORSUM@ACM RecSys 2022: 5th Workshop on Online Recommender
and often less in other domains such as point-of-interest
Systems and User Modeling, jointly with the 16th ACM Conference on recommendations [2]. A third difference is that we ac-
Recommender Systems, September 23rd, 2022, Seattle, WA, USA count for naturally occurring biases in the data such as
Envelope-Open len.feremans@uantwerpen.be (L. Feremans); an imbalance in the popularity and location distributions
robin.verachtert@froomle.com (R. Verachtert); that hinder recommendations based on (context-aware)
bart.goethals@uantwerpen.be (B. Goethals)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License collaborative filtering [1].
CEUR
Workshop
Proceedings
Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
We propose an LTARS that is orthogonal to existing
location-aware and context-aware recommender systems or hybrids thereof [15, 22]. Das et al. propose time-aware
that assume known contextual features or static loca- news recommendations that combine item-based collab-
tional preferences [21, 1, 24] and make the following key orative filtering, pre-filtering news items on recency, and
contributions: age-based discounting to account for the bias towards
recent items [8]. Similarly, we consider the short lifetime
• We propose novel techniques to (1) extract loca- of items, online scalability issues and realistic offline eval-
tion preferences from users based on their fre- uation protocols [3]. A key difference is that we account
quency in the long-term history of geotagged for location preferences and tackle cold-start item recom-
items; (2) identify and rank relevant neighbour mendations. We compare with time-aware collaborative
locations based on collaborative filtering or geo- filtering in Section 4.
graphical distance; (3) filter and rank items jointly In location-based social networks point-of-interests
on recency and popularity; (4) combine location- items are tagged geographically by users interacting on
and time-aware recommendations with collabo- a social network such as Facebook places or Foursquare
rative filtering. [28, 2, 12]. Related to our work, Ye et al. propose a hy-
• The proposed method is straightforward to im- brid recommendation system that models user-item pref-
plement and publicly available1 . It is also highly erences using a combination of collaborative filtering,
efficient supporting frequent or online updates social influence, and geographic modelling where loca-
and cold-start item recommendations. tion preferences are proportional to the inverse squared
• Motivated by the recent criticism of unrealistic geographic distance [28, 18]. However, their approach
offline evaluation protocols [3, 13, 23], we adopt is specific to point-of-interest applications where items
an evaluation protocol based on a sliding win- such as restaurants are rated after users physically check
dow protocol and create subsets of interactions for in at a specific address with longitude and latitude co-
training and testing during consecutive periods ordinates. Location-based social network recommender
where we filter candidate items on publication systems typically also model social influence, assume
date (or first interaction time). explicit feedback and a long life-time of items.
• We find that the proposed method and hybrids
thereof outperform popularity, content-based and
(time-ware) collaborative filtering-based recom- 3. A neighbourhood-based
mender systems on offline and online experi- location- and time-aware
ments for regional new recommendations.
recommender system
2. Related work The proposed method is a combination of different steps
and components that take different signals into account,
In most related work on location-aware recommendations, i.e. spatial, temporal, popularity and social influence, as
general news [24] or Twitter tags [21] are recommended. shown in Figure 1.
In contrast to [24] and extended work [20, 4] we assume
user locational preferences are non-stationary. Pálovics 3.1. Task definition
et al. propose an online model to recommend volatile
items, thereby assuming non-stationary locational pref- Let 𝑈 = {𝑢1 , 𝑢2 , … 𝑢𝑛 } be the set of users and 𝐼 =
erences [21]. Our work is complementary by inferring {𝑖1 , 𝑖2 , … , 𝑖𝑚 } the set of items. We consider implicit feed-
regional preferences for each user. Pálovics et al. also back where a user interacts with a certain item at a
propose a hierarchically organized geolocation structure, certain timestamp, i.e. 𝒟 = {⟨𝑢, 𝑖, 𝑡⟩ | 𝑢 ∈ 𝑈 ∧ 𝑖 ∈ 𝐼 }
while we propose an alternative neighbourhood-based and denote the user history using 𝐼𝑢,𝑡 , i.e. all interacted
method. We argue that focusing on neighbouring loca- items up to timestamp 𝑡. Each item has one or more lo-
tions is essential in applications with a high cardinality cations or geotags 𝐿𝑖 where 𝐿𝑖 ⊆ 𝐿 = {𝑙1 , 𝑙2 , … , 𝑙𝑘 }, e.g.
set of locations, such as regional news recommendations. a specific address, city or country. Each item is avail-
Finally, we use experimental validation in a different able starting from a certain time 𝑡𝑖 , i.e. the publication
domain, i.e. regional news recommendations instead of timestamp. In case this is not available, we compute
geotagged tweets. We find that in both domains, location- 𝑡𝑖 = min({𝑡 | ⟨𝑢, 𝑗, 𝑡⟩ ∈ 𝒟 ∶ 𝑖 = 𝑗}). For time-aware recom-
aware methods outperform popularity, content-based mendations we imitate the online setting as close as possi-
and collaborative filtering-based approaches. ble and evaluate offline based on a sliding window protocol
In time-aware and news recommendations, most algo- where we partition 𝒟 in time given parameters △𝑡train and
rithms are content-based, based on collaborative filtering △𝑡test . That is, we train the model at timestamp 𝑡 using
𝒟train = {⟨𝑢𝑘 , 𝑖𝑘 , 𝑡𝑘 ⟩ | 𝑡 − △𝑡train < 𝑡𝑘 ≤ 𝑡} and predict inter-
1
https://bitbucket.org/len_feremans/regio-reco/
Identify Filter and rank
Pre-processing Create profile
near regions candidate articles
Filter popular Top frequent Geographic Geographic
items (nearby) regions distance
Popularity
Training window Content-based Near regions
using CF Publication
User history datetime
CF
Temporal Location Popularity Social
Figure 1: Overview of proposed LTARS recommender system consisting of 4 main steps. At each step there are different
factors we account for: spatial influence, temporal influence, popularity influence and social influence
actions in 𝒟test = {⟨𝑢𝑘 , 𝑖𝑘 , 𝑡𝑘 ⟩ | 𝑡 < 𝑡𝑘 < 𝑡 +△𝑡test }. Addition- dependencies for determining nearby user-preferred lo-
ally, we define the set of impressionable items at times- cations.
tamp 𝑡 using 𝐶impr = {𝑖 | 𝑖 ∈ 𝐼 ∶ 𝑡 − △𝑡train < 𝑡𝑖 < 𝑡 + △𝑡test }.
The interaction data is represented using a user-item- 3.2.1. Top frequent locations
location matrix as shown in Figure 2. The notations and
definitions used in this paper are summarised in Table 1. Given a user 𝑢 and history 𝐼𝑢,𝑡 at timestamp 𝑡 (i.e. all
The goal is to generate a set of top-𝑛 personalised interactions in 𝒟train ) we count the top-𝑘 most frequent
items most relevant to each user 𝑢 given their history 𝐼𝑢 geotags using:
at timepoint 𝑡.
𝑃 𝑘 (𝑢, 𝑡) = {𝑙 | ∃𝑖 ∈ 𝐼𝑢,𝑡 ∶ 𝑙 ∈ 𝐿𝑖 ∧rank(𝑠𝑢𝑝(𝑢, 𝑙, 𝑡)) ≤ 𝑘} where
3.2. Identifying regional preferences and |{𝑖 | 𝑖 ∈ 𝐼𝑢,𝑡 ∶ 𝑙 ∈ 𝐿𝑖 )}|
sup(𝑢, 𝑙, 𝑡) = .
neighbouring locations |𝐼𝑢,𝑡 |
We assume each user has preferences for multiple loca- The frequency, or support, is a measure of the user-
tions of interest, i.e. their home address, work address location preference, i.e. the ratio of location-specific views
or recent vacation stay. First, we propose a straight- versus all views for that user. We remark that in our ex-
forward technique for determining location preferences periments, recommendations based on the top frequent
using the user’s history based on frequency. Next, we locations improve substantially using a longer history
use location-location and user-location (or collaborative) (e.g. a full month instead of the last days) and after re-
moving the 5% most popular items as very popular items
are detrimental when inferring locational preferences.
l1 l2
i1 i2 i3 i4 i5 3.2.2. Geographical neighbour locations
u1 ✓ ✓ ✓ We define for each location 𝑙𝑖 the top-𝑘 nearest locations
u2 ✓ ✓ using
u3 ✓ ✓ 𝑘 (𝑙 ) = {𝑙 | 𝑙 ∈ 𝐿 ∧ rank(dist(𝑙 , 𝑙 )) ≤ 𝑘}
NGEO 𝑖 𝑗 𝑗 𝑖 𝑗
u4 ✓
where we use dist to denote the geodesic distance be-
Figure 2: Illustration of the user-item-location matrix. Based tween two geotags in kilometres. In practice, we use the
on this example, we infer that user 𝑢1 is primarily interested set 𝐿train ⊆ 𝐿 containing any location associated with
in items from location 𝑙1 , i.e. sup(𝑢1 , 𝑙1 , 𝑡) = 1 and user 𝑢3 in any item in the training data since for many (smaller)
both location 𝑙1 and 𝑙2 , where sup(𝑢3 , 𝑙1 , 𝑡) = sup(𝑢3 , 𝑙2 , 𝑡) = 0.5. locations we have no items published in the past period,
For simplicity, we assume a single geotag per item in the
i.e. cold-start locations and a lower likelihood for future
illustration.
recommendations.
Symbol Description the notation 𝑃(𝑙𝑗 | 𝑙𝑖 ) to denote the conditional probability
𝑈 set of users between two locations, e.g. 30% of users who view items
𝐼 set of items from 𝑙𝑖 also view items from 𝑙𝑗 , defined as:
𝐿 set of locations
𝑢 a user, 𝑢 ∈ 𝑈 𝑀𝑖,𝑗
𝑖 an item, 𝑖 ∈ 𝐼 𝑃(𝑙𝑗 | 𝑙𝑖 ) = where
𝑙 a location, 𝑙 ∈ 𝐿 𝑀𝑖,𝑖
𝐿𝑖 or 𝑙𝑖 set of locations 𝐿𝑖 ⊆ 𝐿 or a single location 𝑙𝑖
association with item 𝑖
𝑀𝑖,𝑗 = ∑ min ( ∑ 1 (𝑙𝑖 ∈ 𝐿𝑎 ) , ∑ 1 (𝑙𝑗 ∈ 𝐿𝑏 )) .
𝑡 a timestamp, often representing current
𝑢∈𝑈 𝑎∈𝐼𝑢,𝑡 𝑏∈𝐼𝑢,𝑡
time
Δ𝑡 an interval in time We remark that locations can re-occur in the history for
𝑡𝑖 publication (or first interaction) timestamp each user and is represented by a multiset, henceforth
of item 𝑖 the number of co-occurrences is the minimum of the
𝒟 set of interactions, i.e. tuples ⟨𝑢, 𝑖, 𝑡⟩ total number of occurrences of each pair of locations
𝒟train set of interactions for training in [𝑡−Δtrain , 𝑡]
aggregated over all users. We define for each location 𝑙𝑖
𝒟test set of interactions for evaluation in ]𝑡, 𝑡 +
Δtest ]
the top-𝑘 nearest locations using:
𝐶impr set of impressionable candidate items at 𝑡, 𝑘 (𝑙 , 𝑡) =
i.e. published before 𝑡 + Δtest
N𝐶𝐹 𝑖 {𝑙𝑗 | 𝑙𝑗 ∈ 𝐿 ∧ rank (𝑃(𝑙𝑗 |𝑙𝑖 )) ≤ 𝑘}
𝐼𝑢,𝑡 set of items viewed by user 𝑢 before times-
tamp 𝑡 3.2.4. Recommending items based on
sup(𝑢, 𝑙, 𝑡) frequency, or support, of location 𝑙 in 𝐼𝑢,𝑡
neighbouring locations
𝑃 𝑘 (𝑢, 𝑡) user-location preferences, i.e. top-𝑘 loca- We extend regional preferences 𝑃 𝑘 (𝑢, 𝑡) to near locations
tions with highest support in 𝐼𝑢,𝑡 using:
𝑃 ′ (𝑢, 𝑡) top-𝑘 most frequent locations and nearby
locations from either 𝑁GEO or 𝑁CF 𝑘
𝑘 𝑃 ′ (𝑢, 𝑡) = 𝑃 𝑘1 (𝑢, 𝑡) ∪ {𝑙𝑖 | ∃ 𝑙 ∈ 𝑃 𝑘1 (𝑢, 𝑡) ∶ 𝑙𝑖 ∈ 𝑁𝛼 2 (𝑙, 𝑡)}
𝑁GEO (𝑙) top-𝑘 geographically nearest locations to 𝑙
𝑃(𝑙𝑗 | 𝑙𝑖 ) conditional probability of visiting location
where 𝑁𝛼 is either 𝑁GEO or 𝑁CF and 𝑘1 and 𝑘2 are hyper-
𝑙𝑗 given 𝑙𝑖 is visited before
𝑘
𝑁CF (𝑙, 𝑡) top-𝑘 nearest locations to 𝑙 based on collab- parameters. We remark that if the profile 𝑃 𝑘 (𝑢, 𝑡) only
orative filtering contains low-density locations, we might have fewer than
𝐶LOC (𝑢, 𝑡) candidate items matching user-location top-𝑛 item recommendations after filtering. Additionally,
preferences locations in 𝑃 𝑘 (𝑢, 𝑡) are based only on the history of the
𝐶POP (𝑡) candidate items matching recency and pop- current users, while we use 𝑁GEO if users are interested
ularity constraints in locations near frequently visited past locations or 𝑁CF
𝐶(𝑢, 𝑡) candidate items matching user-location if users are interested in locations frequently co-visited
preference, recency and popularity by all users.
rank LOC score 𝑖 based on user-location preference Next, we filter impressionable candidate items in 𝐶impr
rank POP+REC score 𝑖 based on recency and popularity
matching location regional preferences. Given a user 𝑢
rank LTARS score 𝑖 based on user-location preference,
recency and popularity
and regional preferences 𝑃 ′ (𝑢, 𝑡) we define the set of
rank CF score 𝑖 based on collaborative filtering filtered candidate items at timestamp 𝑡:
Table 1 𝐶LOC (𝑢, 𝑡) = {𝑖 | 𝑖 ∈ 𝐶impr ∶ 𝐿𝑖 ∩ 𝑃 ′ (𝑢, 𝑡) ≠ ∅}.
Overview of notation and definitions
We rank items in 𝐶LOC (𝑢, 𝑡) based on the user-location
preference score using:
3.2.3. Collaborative filtering-based neighbour
locations 𝑠𝑢𝑝(𝑖, 𝑙𝑖 , 𝑡) if 𝑙𝑖 ∈ 𝑃 𝑘 (𝑢, 𝑡)
⎧ dist(𝑙𝑖 ,𝑙𝑗 )
⎪sup(𝑢, 𝑙 , 𝑡) ⋅ (1 − )
An alternative technique is to learn nearby locations ⎪ 𝑗 max({dist(𝑙 ,𝑙 ) | 𝑙 ∈N 𝑘 (𝑙 )})
𝑗 𝑘 𝑘 GEO 𝑗
rankLOC (𝑢, 𝑖, 𝑡) =
based on the geotagged item histories of all users, result- ⎨ if 𝑙𝑖 ∈ 𝑃 ′ (𝑢, 𝑡) ∧ 𝑁𝛼 = 𝑁GEO
ing in because you interacted with location 𝑙𝑖 we recom- ⎪ sup(𝑢, 𝑙𝑗 , 𝑡) ⋅ 𝑃(𝑙𝑖 |𝑙𝑗 )
⎪
mend location 𝑙𝑗 type of inference. Hence, we compute ⎩ if 𝑙𝑖 ∈ 𝑃 ′ (𝑢, 𝑡) ∧ 𝑁𝛼 = 𝑁CF .
the co-visitations for each location similar to item-based
collaborative filtering but counting co-occurrences of the In case a candidate item 𝑖 is tagged with multiple locations
location of items instead of the items themselves. We 𝐿𝑖 , we use the location having the maximal score w.r.t. to
store co-occurrence counts in a matrix 𝑀 |𝐿|×|𝐿| . We use the user for estimating relevance, i.e. argmax sup(𝑢, 𝑙𝑗 , 𝑡).
𝑙𝑗 ∈𝐿𝑖
For example, assume 𝑢1 has interacted with 100 items 𝑖1 was published 15 minutes ago with 0 views and 𝑖2
of which 50 are tagged with 𝑙1 and 20 with 𝑙2 during the was published 5 hours ago with 100 views. Again we
training period [𝑡 − △𝑡train , 𝑡[. Assume that 30% of all rank 𝑖1 before 𝑖2 since rankPOP (𝑖1 , 𝑡) = 0+10
0.25
= 40 and
users who interacted with 𝑙1 also interacted with 𝑙3 . The rankPOP (𝑖2 , 𝑡) = 100+10 = 22.
user-location preferences are then rankLOC (𝑢1 , 𝑙1 , 𝑡) = 5
0.5, rankLOC (𝑢1 , 𝑙2 , 𝑡) = 0.2 and rankLOC (𝑢1 , 𝑙3 , 𝑡) = 0.5 ×
0.3. 3.4. Combining location- and time-aware
recommendations with collaborative
3.3. Ranking items on popularity and filtering
recency This section proposes combinations of location-, time-
This section considers strategies to filter and rank items aware and collaborative filtering-based recommenda-
on popularity and recency. A popularity filter keeps can- tions.
didate items above a certain popularity threshold. A
recency filter keeps candidate items published before a 3.4.1. Location- and time-aware recommendations
specific timestamp. Both approaches have their draw- First, we propose to combine scores to recommend items
backs, i.e. a popularity filter removes cold-start (or very having relevant geotags and are trending. We define the
recent) items, while a recency filter removes slightly aged set of candidate items by filtering on both regional pref-
yet popular items. We define the set of candidate items erences, popularity and recency: 𝐶(𝑢, 𝑡) = 𝐶
LOC (𝑢, 𝑡) ∩
filtered on both recency and popularity at timestamp 𝑡 𝐶 (𝑡). For a user 𝑢 the candidate items 𝑖 ∈ 𝐶(𝑢, 𝑡) are
POP
as: ranked using:
𝐶POP (𝑡) = {𝑖 | 𝑖 ∈ 𝐶impr ∶ (𝑡 − △𝑡1 < 𝑡𝑖 ) ∧ (𝑡 − △𝑡2 < 𝑡𝑖 ∨ rankLTARS (u, i, t) =
pop(𝑖, 𝑡, △𝑡pop ) > 𝜖) where 𝛼 ⋅ rankLOC (𝑢, 𝑖, 𝑡) + (1 − 𝛼) ⋅ rank POP+REC (𝑖, 𝑡)
pop(𝑖, 𝑡, △𝑡pop ) = |{⟨𝑢𝑘 , 𝑖𝑘 , 𝑡𝑘 ⟩ | ⟨𝑢𝑘 , 𝑖𝑘 , 𝑡𝑘 ⟩ ∈ 𝒟train ∶ where
𝑖𝑘 = 𝑖 ∧ 𝑡𝑘 > 𝑡 − △𝑡pop }|. rankPOP+REC (𝑖, 𝑡)
rank POP+REC (𝑖, 𝑡) =
max({rankPOP+REC (𝑗, 𝑡) | 𝑗 ∈ 𝐶(𝑢, 𝑡)})
Here, △𝑡1 , △𝑡2 , △𝑡𝑝𝑜𝑝 and 𝜖 are hyper-parameters. For in-
stance, by selecting △𝑡1 = 4𝑑, △𝑡2 = 2𝑑, △𝑡pop = 4𝑑, and where 𝛼 is a hyper-parameter to control the relative
𝜖 = 1 we exclude items that are more than 4 days old or weight of a user’s regional preferences versus the popu-
between 2 to 4 days old and have fewer than 1 interac- larity of an item normalised over its age.
tions during the last 4 days. For brevity of this paper we
omit detailed experiments on the effect of selecting can- 3.4.2. Online computation of location- and
didate using 𝐶POP . But we remark that on the regional time-aware recommendations
news dataset, we have a recall@10 of 0.221 when using
the default set of impressionable items 𝐶impr , which in- An advantage of the proposed approach for location-
creases to 0.231 (+4.5%) by filtering articles older than two and time-aware recommendation is that it can be com-
days and to 0.239 (+8.1%) using 𝐶POP w.r.t. the previous puted online. For updating 𝑃 𝑘 (𝑢, 𝑡) for each user we store
parameters. the frequency of each location in memory and update
Traditionally, we rank candidate items on recency, i.e. counts when new interactions arrive. Since, NGEO is time-
publication timestamp or popularity. However, both ap- independent, we precompute the pairwise distances once
proaches have their disadvantages. We propose to rank offline having a complexity of 𝑂(|𝐿|2 ) and store the result-
candidate items 𝑖 on recency and popularity jointly using: ing matrix. For computing neighbouring locations based
on collaborative filtering, the complexity is 𝑂(|𝒟 | + |𝐿|2 ).
pop(𝑖, 𝑡, △𝑡pop ) + 𝑐 Algorithms exist to update the item similarities incremen-
rank POP+REC (𝑖, 𝑡) = tally [14], which in principle could be adopted for updat-
𝑡 − 𝑡𝑖
ing location similarities online. However, since location
where we normalise the popularity by the number of neighbourhoods are typically less dynamic, the need for
hours since the publication and where 𝑐 represents a bias frequent model updates is less important. Therefore,
term for cold-start items. For instance, given item 𝑖1 that we choose to re-compute the co-visitation matrix reg-
was published 1 hour ago with 100 views and an item 𝑖2 ularly, thereby adopting sparse optimisation techniques
published 5 hours ago with 200 views. We rank 𝑖1 before that make this computation extremely efficient, even on
𝑖2 since on average more users have viewed 𝑖1 items in large datasets [11]. Finally, we update the popularity
a single hour. As a second example, assume 𝑏 = 10 and counts of each item online. We remark that online model
training is essential for both computational efficiency is usually more of it where many regions and towns have
and accuracy, since in many domains, such as social me- multiple articles published every day.
dia, news or auction websites recent items are the most We load all interactions and article metadata during a
relevant. 40-day period (from 1st July until 11th August 2021) and
exclude all articles containing general news and sport.
3.4.3. Hybrid recommendations Next, we filter items and users having fewer than five
interactions and items that are viewed more than once
A limitation of the previous approach is that two users by the same user. By default, we remove candidate items
with the same regional preferences 𝑃 ′ (𝑢, 𝑡) receive the that are more than 4 days old. We remove the overall top
same recommendations at timestamp 𝑡. A natural exten- 1% most popular items to overcome that the recommen-
sion is to adopt existing time-aware collaborative filtering dation model is biased towards predicting only popular
methods [8] and have a hybrid solution where we also items. After pre-processing, we have 7.6 million interac-
account for user-item preferences. Therefore, we adopt tions, 458 755 users and 9 493 items. Next, we perform
item-based collaborative filtering as a strong baseline an offline simulation where we evaluate recommenda-
[10, 6]. We compute conditional probabilities for each tions using a sliding window of 2 hours (△𝑡
test = 2ℎ) in
item pair based on co-visitations and apply exponential the last week of data since the popularity distribution
age-based discounting to give more weight to recent in- (and results) vary substantially in time [23]. At each two-
teractions [8, 17]. We define a weighted co-visitation hourly interval, we train a model based on interactions
matrix 𝐹 |𝐼 |×|𝐼 | using: in the past △𝑡train days and report recall@10 and ndcg@10
𝑡 𝑡 during the entire test week for users with interactions in
𝐹𝑖,𝑗 = ∑ 𝑤𝑢,𝑖 ⋅ 𝑤𝑢,𝑗 where
both the train and test set.
𝑢∈𝑈
𝑡−𝑡𝑢,𝑘
𝑡 = {𝑎 ⋅ (1 − 𝑏)
𝑤𝑢,𝑘
if 𝑖𝑘 ∈ 𝐼𝑢,𝑡
}.
4.1.2. Public datasets
0 otherwise
We also use two public location-based social network
Here, 𝑡 − 𝑡𝑢,𝑘 denotes the difference in hours between the datasets to promote reproducibility. The datasets contain
current time 𝑡 and the time of interaction 𝑡𝑢,𝑘 transformed227,428 and 573,703 check-ins collected for 10 months
using an exponential time-decay function parameterised from Foursquare in New York City and Tokyo [27]. Each
by 𝑎 and 𝑏. For collaborative filtering-based recommen- check-in is associated with a venue (or item), timestamp,
dation we compute a score: GPS coordinates and category, which we ignore. Since
the dataset is relatively small, we use a single time-based
𝐹𝑖,𝑗
rankCF (𝑢, 𝑖, 𝑡) = ∑ 𝑃(𝑖 | 𝑗) = ∑ split and use the last month for evaluation. We pre-
𝑗∈𝐼𝑢,𝑡 𝑗∈𝐼𝑢,𝑡 𝐹𝑗,𝑗 + 1 process the dataset as before and filter items and users
with fewer than five interactions and items viewed more
where 𝑃(𝑖|𝑗) denotes the conditional probability between than once. Additionally, we round GPS coordinates to 2
two items, e.g. 30% of users who (recently) viewed article decimals to create location tags, thereby considering all
𝑗 also (recently) viewed article 𝑖. coordinates within 1.11 kilometres identical. The main
Finally, we recommend items based on both location- properties of each dataset are shown in Table 2.
and time-aware preferences and collaborative filtering,
i.e. given a user 𝑢 and a candidate item 𝑖 ∈ 𝐶(𝑢, 𝑖), we
4.2. Comparing regional preferences and
define:
neighbouring locations determined
rankLTARS+CF (𝑢, 𝑖, 𝑡) = using geodistance and collaborative
𝛽 ⋅ rankLTARS (𝑢, 𝑖, 𝑡) + (1 − 𝛽) ⋅ rankCF (𝑢, 𝑖, 𝑡). filtering
In this first experiment, we investigate if users are more
4. Experimental setup and results interested in geotagged items that are nearby geograph-
ically or from similar locations based on collaborative
4.1. Dataset and offline evaluation filtering. We investigate the following methods on the
4.1.1. Regional news regional news dataset:
We collect data from a prominent regional newspaper 1. Using the top-𝑘 most frequent regions, i.e. 𝑃 𝑘 (𝑢, 𝑡)
in Belgium. In the current digital age more and more without nearby regions.
users look for information on newspaper websites which 2. Add geographically near locations from NGEO .
brings several challenges for the recommendation system. 3. Add near locations based on collaborative filter-
Regional news is different from general news in that there ing from NCF .
Dataset #users #items #locations #interactions
Regional news 458 755 9 493 329 7 649 178
Foursquare TKY 2 292 7 057 732 128 555
Foursquare NYC 1 083 3 908 527 40 935
Table 2
Properties of datasets after preprocessing.
′
For extending the profile 𝑃 (𝑢, 𝑡) we select hyperparame- 4.4. Comparing popularity, time-aware
ters 𝑘1 = 3 and 𝑘2 = 3, use Δtrain = 30𝑑 and filter the top collaborative filtering, content-based
5% most popular items before learning regional prefer- filtering and location- and
ences and the location similarity matrix. We rank candi-
date items matching regional preferences on recency. time-aware recommendation systems
In Figure 3, we show each method result’s for ndcg@10 In this experiment, we compare the following recommen-
and recall@10. The mean ndcg@10 is 0.184 when using dation systems on the proprietary regional news dataset
collaborative filtering, 0.222 when using geographically and two public datasets:
nearby locations and 0.203 when only using the top-k fre-
quent locations from the history. Therefore, we observe 1. A popularity baseline ranking the most trending
a relative increase of 8.5% using nearby regions deter- items.
mined using geodesic distance. A similar trend holds 2. Content-based filtering ranking the most similar
for recall@10. We observe that adding nearby locations items using soft-cosine based on a pre-trained
using collaborative filtering does not perform well in this word2vec embedding [26].
dataset. However, we argue that this variant has poten- 3. Item-based collaborative filtering with age-based
tial in other applications, such as Twitter tag predictions discounting [8, 17].
[21], where locations are more distant and international. 4. LTARS with geographically near locations and
ranking jointly on user-location preference and
recency and popularity.
4.3. Comparing ranking methods
5. A hybrid recommender where we combine
Intuitively purely ranking on recency as we do in the LTARS with item-based collaborative filtering.
previous experiment does not result in the best top-𝑛
recommendations. In this experiment, we compare the 4.4.1. Regional news dataset
following ranking methods on the regional news dataset:
We select hyperparameters △𝑡pop = 12ℎ for popularity,
1. On recency △𝑡train = 3𝑑 for collaborative filtering and △𝑡train = 30𝑑
2. On popularity for LTARS. For collaborative filtering we set the weight-
3. On rankPOP+REC decay parameters to 𝑎 = 1 and 𝑏 = 0.1. For LTARS we use
4. On rankLTARS the extended profile 𝑃 ′ (𝑢, 𝑡) where we use the top-3 most
frequent regions (𝑘1 = 3) and top-3 geographically near-
We filter candidate items using the top-3 most frequent
est neighbours (𝑘2 = 3) and for ranking we set 𝛼 to 0.25
locations for each user and set hyperparameters △𝑡pop =
thereby giving more relatively more weight to the user-
12ℎ for the popularity window, 𝑐 = 0 for rankPOP+REC
location preference score. For the hybrid recommender
and 𝛼 = 0.5 for rankLTARS giving equal weight to the user-
we set 𝛽 to 0.5.
location preference score rankLOC and rankPOP+REC .
The resulting ndcg@10 and recall@10 values over a
In Figure 4, we show the results for ndcg@10 and re-
week are shown in Figure 5 and the average values in
call@10. The mean ndcg@10 is 0.205 by ranking on pop-
Table 3. Concerning ndcg@10 the hybrid method works
ularity, 0.204 using recency and 0.218 using rankPOP+REC
best, i.e. with an ndcg@10 of 0.270 we observe a 6.2%
(+5.9%). If we rank using rankLTARS the ndcg@10 in-
increase over item-based collaborative filtering, a 13.7%
creases to 0.226 (+9.3%). We remark that by ignoring
increase over LTARS, and a 50% increase over popularity.
recency and ranking on user-location preference score
We omit the results from the plot for the content-based
only the ndcg@10 decreases to 0.122. The recall@10
recommender: with an ndcg@10 of only 0.019 it performs
is respectively 0.285, 0.296, 0.300 and 0.309 by ranking
poorly. With a recall@10 of 0.301 the proposed LTARS
on popularity, recency, rankPOP+REC and rankLTARS . We
method has the highest recall@10 and we observe a 4.3%
conclude that ranking items on user-location preference
increase compared to item-based collaborative filtering, a
and popularity/age outperforms baseline ranking func-
24.9% increase compared to the popularity baseline, and a
tions by a wide margin.
small 1.3% increase compared to the hybrid recommender.
0.30
Pk
P k + NGEO
0.25 P k + NCF
ncdg
0.20
0.15
0.10
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
time
0.40 Pk
P k + NGEO
0.35 P k + NCF
0.30
recall
0.25
0.20
0.15
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
time
Figure 3: Ndcg@10 and recall@10 over one week for different methods for determining regional preferences and neighbouring
locations on the regional news dataset. Using the top-3 frequent location the mean ndcg@10 is 0.203, by adding near location
from NGEO the ndcg@10 increases to 0.222 (+8.5%), and by adding nearby locations from NCF the ndcg@10 decreases to 0.184
(-9.3%).
rankLTARS
0.30 rankPOP + REC
recency
0.25 popularity
ncdg
0.20
0.15
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
time
rankLTARS
0.40 rankPOP + REC
recency
0.35 popularity
recall
0.30
0.25
0.20
0.15
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
time
Figure 4: Ndcg@10 and recall@10 over one week for different methods for ranking candidate items on the regional news
dataset. By ranking on popularity results the a mean ndcg@10 is 0.205 and on recency the mean ndcg@10 is 0.204. By ranking
on rankPOP+REC the ndgc@10 increases to 0.218 (+5.9%). By ranking on rankLTARS the ndcg@10 increases to 0.226 (+9.3%).
Interestingly, there is no clear winner over the entire LTARS we use the extended profile 𝑃 𝑘 (𝑢, 𝑡) where we use
week, i.e. on the last day we observe a severe drift in the top-20 most frequent regions and set 𝛼 to 0.75. For
popularity bias better captured by collaborative filtering the hybrid recommender we set 𝛽 to 0.5.
since LTARS filters out (popular) items not matching We show the results in Table 3. We find that, concern-
regional preferences. However, the accuracy of LTARS ing ndg@10 the hybrid method performs best, followed
validates the premise that local items are often more by LTARS with a large margin. Concerning recall@10
relevant. LTARS performs worse than the popularity baseline. We
remark that by ignoring ranking on item recency would
4.4.2. Public datasets further improve results. We conclude that our method
outperforms the popularity and item-based collaborative
We repeat the previous experiment using two public filtering methods by a large margin concerning ndcg@10.
location-based social network datasets. We remark that
in both datasets, there is no significant preference to-
4.4.3. Execution time
wards more recent venues. We set the popularity and
training window to use all available data, i.e. we select In Table 4 we show the total execution time in seconds for
hyperparameters △𝑡pop = △𝑡train = 9𝑚 for collaborative training the model and computing predictions. Runtimes
filtering and LTARS and do not use weight-decay. For are measured on a laptop with an 2,3 GHz 8-Core Intel
0.35
0.30
0.25
ncdg
0.20
0.15 rankLTARS + CF
rankLTARS
0.10
rankCF
0.05 popularity
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
0.5 time
0.4
0.3
recall
0.2 rankLTARS + CF
rankLTARS
rankCF
0.1 popularity
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
time
Figure 5: Ndcg@10 and recall@10 for different recommendations techniques over a week on the regional news dataset. The
mean ndcg@10 is 0.135 for the popularity baseline, 0.253 for item-based collaborative filtering, 0.233 for LTARS and 0.270 for
the hybrid method. The mean recall@10 is 0.226 for the popularity baseline, 0.288 for item-based collaborative filtering, 0.301
for LTARS and 0.297 for the hybrid method. We omit the results for the content-based recommender having only a mean
ndcg@10 of 0.019.
Dataset Popularity ItemKNN LTARS Hybrid
ndcg@10:
Regional news 0.135 0.253 0.233 0.270
Foursquare TKY 0.034 0.077 0.095 0.129
Foursquare NYC 0.031 0.052 0.084 0.106
recall@10:
Regional news 0.226 0.288 0.301 0.297
Foursquare TKY 0.042 0.042 0.036 0.040
Foursquare NYC 0.043 0.041 0.028 0.033
Table 3
Comparing top-𝑛 recommender systems on private and public datasets. The best result on each dataset are in bold (best) or
underlined (second best).
Core i9 and 16 GB of RAM. The publicly available im- 4.5. Online evaluation
plementation is in Python. We remark, that concerning
To validate the findings of the proposed approach we per-
complexity, model training is 𝑂(|𝐼 |2 ) for item-based col-
formed an online A/B trial on two regional news websites
laborative filtering and 𝑂(|𝐿|2 ) for LTARS with geodesic
in Belgium. The goal of the recommendations is to sur-
nearby regions. At test time methods have a compa-
face relevant regional articles for each user. Users have
rable cost, i.e. for LTARS we filter items on regional
the option to explicitly specify one or more locations
preference and compute the user-location preference and
they are interested in, however only 1 in 4 users provide
popularity-based scores for each item, while for item-
this preference. Finding the right regions to recommend
based collaborative filtering we compute the dot product
articles from, therefore is an important problem for these
between the history and the (time-weighted) item-item
websites.
similarity matrix. In the Regional news datasets we have
During the trials users were randomly assigned to ei-
6 624 156 interactions, 456 578 users and 8 462 items in
ther the control or treatment group during the test period
the first window, yet total training time is only 27.3𝑠. For
of 9 days. Both groups received an equal amount of users.
making predictions we require 14.1s for 22 921 test users,
The control algorithm recommends the most recent items
which is less then 1 millisecond per user on average. We
from the explicit interest locations if available and oth-
conclude that LTARS is highly efficient and scalable to
erwise recommends articles from each user’s most read
large datasets.
region. In the experimental group a user profile 𝑃 𝑘 (𝑢, 𝑡)
is constructed with 𝑘 = 3 following the LTARS method
described in this work, ignoring the 1% most popular
Dataset Popularity ItemKNN LTARS Hybrid
runtime (s)
Regional news 3.6s 141.7s 34.7s 182.4s
Foursquare TKY 0.4s 4.8s 2.4s 8.4s
Foursquare NYC 0.8s 14.0s 7.5s 25.3s
Table 4
Comparing the execution time of top-𝑛 recommender systems on private and public datasets.
items. If a user has given an explicit regional preference, Parame- Description
these regions are always included in their regional profile. ter
So a user that indicated explicit interest in two regions, Δ𝑡train training window
will have a third region deduced from their historical Δ𝑡test test window
Δ𝑡pop training window for computing popularity
interactions. For this group items are filtered on regional
𝑘1 top-𝑘1 locations with highest frequency
preferences and ranked according to recency.
𝑘2 top-𝑘2 nearby locations in 𝑁GEO or 𝑁CF
A total of 1.7 million boxes were requested for 235k 𝑐 bias term in rank POP+REC (𝑖, 𝑡)
users on the first newspaper and 2.5 million boxes for 𝜖 threshold when filtering candidate items
375k users on the second. We find that the experimental on popularity
group has a 5.1% (relative) increase in click-through-rate 𝑎, 𝑏 weights for exponential decay in time-
on the first newspaper and an 11.4% increase on the sec- aware item-based collaborative filtering
ond. Both results were statistically significant at the 99% 𝛼 relative weight for ranking on regional pref-
confidence level. In addition to the CTR results, the re- erences versus popularity and recency
gional profiles also cover more of the user’s regions of 𝛽 relative weight for ranking on location and
interest, recommending them articles from more diverse time versus collaborative filtering
regions. Table 5
Overview of hyperparameters
4.6. Sensitivity of hyperparameters
In Table 5 we summarise the hyperparameters introduced
to evaluate the weighted sum w.r.t. 𝛼 and 𝛽 using the
by the proposed LTARS. We investigate the sensitivity of
cached partial scores for location, recency and collabora-
our model with respect to the most important parameters,
tive filtering-based recommendations for each user, item
𝛼 and 𝛽 for giving more weight to location, recency or
pair.
collaborative filtering. To clearly show the influence
of these parameters, we report recall@10 with different
parameter settings on two datasets. 5. Conclusion
In Figure 6 we plot the recall@10 for varying values
of 𝛼 and 𝛽 between 0.0 and 1.0 while keeping other pa- In this paper, we tackle the important problem of opti-
rameters fixed as discussed before (i.e. Δ𝑡train = 30𝑑, mising location- and time-aware recommendations. We
Δ𝑡pop = 12ℎ, 𝑘1 = 3 and 𝑐 = 0 for Regional news). On propose techniques for determining regional preferences
the Regional news dataset we find that the hand-picked and neighbouring locations of interest. Additionally, we
values for 𝛼 and 𝛽 in the last experiment are sub-optimal. consider ranking functions that consider spatial, tempo-
With a value of 𝛼 set to 0.75 the recall@10 is 0.361 which ral and behavioural factors. We performed an extensive
increases to 0.378 (+ 4.6%) using the optimal value of comparison offline using a realistically time-aware pro-
𝛼 = 0.3 and with a value of 𝛽 = 0.5 the recall@10 is 0.348 tocol based on sliding windows. Experiments show that
which increases to 0.360 (+ 3.4%) using 𝛽 = 0.9. A similar the neighbourhood-based location- and time-aware rec-
trend is visible in Foursquare TKY where also a local ommendation system and hybrids thereof outperform
maximum is found with values of 𝛼 and 𝛽 in between. popularity, content-based and time-aware collaborative
This suggests that we should optimise hyperparameters filtering-based methods on a large regional news dataset
to further increase accuracy. We remark that in a dy- and two public location-based social network datasets.
namic environment where there is a potential drift in Additionally, we performed an online A/B trial showing
popularity bias, it make sense to adjust hyperparame- a clear increase in click-through-rate.
ters periodically, i.e. using the last batch (or window) A limitation of our work is that the proposed model is
of interactions for tuning. Note that optimising 𝛼 and straightforward and many of the proposed components
𝛽 is computationally inexpensive since we only have consist of heuristics. We motivate this by the fact that for
rankLTARS 0.36 rankLTARS + CF
0.375
0.35
0.370
0.34
recall@10
recall@10
Regional news 0.365
0.33
0.360 0.32
0.355 0.31
0.350 0.30
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
α β
0.035 rankLTARS + CF
0.042
0.030
0.040
recall@10
recall@10
Foursquare TKY 0.025
0.020 0.038
0.015 0.036
rankLTARS
0.010
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
α β
Figure 6: Recall@10 on the Regional news (up) and Foursquare Tokyo (bottom) datasets for varying hyperparameter. In
the first experiment (left) we vary 𝛼 between 0.0 (ranking only on rankPOP+REC ) and 1.0 (ranking only on rankLOC ). In both
datasets we observe a local maximum for 𝛼 in between, advocating that ranking on both popularity/recency and user-location
preferences is important. In the second experiment (right) we varying 𝛽 between 0.0 (ranking only on collaborative filtering) and
1.0 (only on rankLTARS ). Again, we observe a local maximum for 𝛽 in between, advocating that the hybrid method outperforms
LTARS and CF-based recommendations.
location- and time-aware recommendation, the implicit and robust baseline when comparing future research in
interaction data is biased and extremely sparse where we online location- and time-aware recommender systems.
have relatively few interactions specific to one period and For future research, it would be of interest to update pa-
location. Moreover, Dacrema et al. have recently shown rameters dynamically, i.e. by selecting the best value of
that well-tuned simple baselines, such as ItemKNN, are 𝛽 and 𝛼 based on the evaluation of those parameters on
difficult to beat when using more realistic evaluation the previous period and adapt to the current temporal
strategies [6]. A second limitation is that the method is context, i.e. drift in the popularity distribution.
specific to applications where items are tagged with one
or more locations and the volatility of items is crucial. Acknowledgements
We find that the intrinsic simplicity and heuristic na-
ture make our model efficient to compute online and the The authors would like to thank the VLAIO project on
capacity to predict fresh and cold-start items. We con- qualitative evaluation for online recommender systems
clude that the proposed algorithm is useful as an efficient for funding this research.
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