=Paper=
{{Paper
|id=Vol-1906/paper4
|storemode=property
|title=Towards the Recommendation of Personalised Activity Sequences in the Tourism Domain
|pdfUrl=https://ceur-ws.org/Vol-1906/paper4.pdf
|volume=Vol-1906
|authors=Gunjan Kumar,Houssem Jerbi,Michael O’Mahony
|dblpUrl=https://dblp.org/rec/conf/recsys/KumarJO17
}}
==Towards the Recommendation of Personalised Activity Sequences in the Tourism Domain==
https://ceur-ws.org/Vol-1906/paper4.pdf
Towards the Recommendation of Personalised Activity
Sequences in the Tourism Domain
Gunjan Kumar, Houssem Jerbi, and Michael P. O’Mahony
Insight Centre for Data Analytics,
School of Computer Science, University College Dublin, Ireland
{firstname.lastname}@insight-centre.org
ABSTRACT lifelogging and urban computing domains, where activities included
In this paper we consider the problem of recommending sequences socialising, eating, etc. and modes of transport, respectively. In
of activities to a user. The proposed approach leverages the order as this paper, we extend the activity recommendation framework to
well as the context associated with the user’s past activity patterns address the task of recommending a sequence of activities to the
to make recommendations. This work extends the general activity user. Moreover, we apply our framework to the tourism domain,
recommendation framework proposed in [16] to iteratively recom- where a recommended sequence of activities might be, for example,
mend the next sequence of activities to perform. We demonstrate visiting a zoo, eating Italian food, and then listening to live music.
the efficacy of our recommendation framework by applying it to the Our work is motivated by the assumption that people tend to
tourism domain and evaluations are performed using a real-world repeat similar patterns of activities under similar circumstances
(checkin) dataset. [29]. Hence, in order to infer the next activities for a user, it is
important to consider the activity patterns performed in the past. At
CCS CONCEPTS the same time, the context surrounding these activities significantly
affects the next activities the user performs. The importance of
• Information systems → Recommender systems; Decision
modelling context has been recognised in both tourism [6, 17, 18]
support systems; Spatial-temporal systems;
as well as recommender systems research [1]. Context is particularly
important in tourism as the user is predominantly mobile [10]. For
KEYWORDS
example, features such as the time of day, location and weather can
Sequence Recommendation, Recommender Systems, Activity Rec- determine whether a user visits a particular amusement park in the
ommendation, Activity Timeline Matching city or not.
In recommender systems research, the task of recommending
1 INTRODUCTION sequences is comparatively under-explored [13, 27]. However, there
exists works, particularly for points of interest/itinerary (LBSN)
Internet and digital technologies have significantly influenced the
[20, 30, 34] and music playlists [2, 4, 8, 22, 23, 27] recommendation,
tourism sector in the last decade resulting in a steady growth in
which address this task. A popular approach for modeling sequences
e-tourism [7]. Users now have easy access to vast amounts of infor-
has been Markov-based models [4] and all-kt h -order Markov mod-
mation on the web which assists them to plan trips, make reserva-
els [3, 9, 25, 28]. However, in general, these approaches are not
tions, and purchase products etc. However, the number of available
suitable for modelling sequences of activities with multiple features
choices have increased so rapidly that it has become difficult to find
or context and are limited to the Markov assumption which does
the right information at the right time. Thus, recommender systems,
not apply in all cases [4]. An alternative hierarchical graph-based
which have found immense success in e-commerce, have the po-
approach to capture sequences and geographical hierarchies in lo-
tential to play a crucial role in e-tourism by providing personalised
cation trajectories is presented in [19]. This is further enhanced in
and relevant content to users [5, 14, 24, 27].
[35] by modeling location popularity and user experiences to mine
To provide useful recommendations, it is essential to capture the
popular travel sequences across users in a non-personalised man-
behaviour and needs of users, which has been particularly challeng-
ner. Similarly, graph-based models have been used for collaborative
ing in e-tourism [26]. However, as digital technologies have now
itinerary recommendation [33]. However, these approaches do not
permeated our daily lives to a great extent, many aspects of our lives
capture the context information associated with user activities.
can now be easily recorded in digital format. For example, physi-
The key distinguishing characteristic of our work is that the
cal activities performed, locations visited and media consumed by
model captures both the past activities of users, together with the
users can be recorded using mobile devices [12]. Moreover, mobile
context associated with these activities, in order to recommend
personal assistants, such as Google Now and Microsoft Cortana, are
the next sequence of activities for users to perform. The main
capable of passively recording the digital activities of users. These
contributions of this work can be summarised as follows:
recordings, which contain the activity patterns and preferences of
users, can facilitate the development of personalised recommender
systems capable of generating recommendations at the right time • The extension of the generic activity recommendation frame-
and in the right way for a given user and context [11, 31, 32]. work in [15, 16] to recommend the next sequence of activi-
In our previous work [15, 16], we proposed a generic activity ties that should be performed by users. For this, an iterative,
recommendation framework to recommend the next activity to content-based recommendation approach is proposed, which
perform to a user. Our approach was applied successfully in the takes the sequence as well as the features associated with
RecTour 2017, August 27th, 2017, Como, Italy. 26 Copyright held by the author(s).
previous activity occurrences into consideration to build the
Algorithm 1: SeqNCSeqRec
recommendation model (Section 2);
• The application of our proposed algorithm to the tourism Input: User, u; user’s past timeline, T ; recommendation time, RT ;
domain. Experiments using a location checkin dataset [21] current activity object, aoc ; N -count value, N
demonstrate the efficacy of our approach in recommend- Output: a recommended timeline (sequence) Tr ec of L activity
ing sequences given a diverse variety of activities and user objects, Tr ec =< aor ec 1 , aor ec 2 , ...aor ec i ..., aor ec L >
activity patterns (Section 3). 1. Extract the current timeline Tc from T ; the final element of
Tc is aoc
2. Tr ec ← < >
2 RECOMMENDATION APPROACH 3. i ← 1
In this section, we formulate the problem of recommending the 4. while i ≤ L do
next sequence of activities to a user. These activities can be, for ex- 5. Extract candidate timelines T from T (each
j
ample, eating Italian food, shopping at a bookstore, listening to live Tj ∈ T ends with an activity object ao f such
j
music, etc. The proposed content-based sequence recommendation that ao f .name = aoc .name)
algorithm leverages sequential patterns in a user’s past activities 6. R←{}
as well as the contextual information (for example, time of day, 7. for each Tj ∈ T do
location, weather, etc.) associated with each activity occurrence. j
R ← R ∪ ao f +1
8. for each ao ∈ R do
2.1 Problem Formulation Compute Score(ao)
We introduced the concept of an activity object and an activity time- 9. aor ec i .name ← top-1(ao.name : ao ∈ R)
line in [15]. An activity object, aoi , refers to a single 10. Compute and assign features to aor ec i
occurrence of Tr ec ← append(Tr ec , aor ec i )
an activity and consists of a set of features, aoi = vi1 , vi2 , ..., vim , 11.
which describe the context surrounding that particular occurrence 12. Tc ← append(Tc , aor ec i )
of the activity. For example, an activity object can refer to an in- 13. RT ← aor ec i .time
stance of ‘a visit to a zoo’ (i.e. the activity name) with associated 14. i ←i +1
contextual features, such as time of day, geo-location, weather, popu- 15. return Tr ec
larity of the location, etc. An activity timeline (or timeline for short)
for a user is then a chronological sequence of all activity objects From this set of scored activity objects, the top-1 activity name
performed by that user, T =< ao 1 , ao 2 , ..., aon >. with the highest score is returned as the name for aor ec i in Tr ec
(Step 9). The values for the other features of aor ec i are then com-
puted (Step 10) based on the average values for each feature from
2.2 Recommendation Algorithm the user’s past timeline. For example, if the recommended activity
The proposed recommender is based on previous work [16], in name is eating ‘Italian Food’, the time at which this activity should
which the past activities performed by a user were modelled as a occur (aor ec i .time) is calculated as follows. The median difference
timeline, T , and the objective was to recommend the next activity between all occurrences of ‘Italian Food’ and the immediately pre-
to a user to perform. Here, we extend this approach to recom- ceeding activity in the user’s past timeline is calculated; aor ec i .time
mend the next sequence of activities for users to perform, Tr ec =< is then given by the current recommendation time (RT ) plus this
aor ec 1 , aor ec 2 , ..., aor ec L >. difference.
Referring to Algorithm 1, a sequence of activities at a given rec- Before the next iteration of the algorithm, aor ec i is appended
ommendation time (RT ) are generated as follows. The most recent to the current timeline Tc (and becomes the current activity object
activity object performed by the user, referred to as the current in the next iteration) (Step 12) and the recommendation time (RT )
activity object, aoc , is initialised as the activity object occurring is set to aor ec i .time (Step 13). Thus, the L activity objects in the
at time RT in the user’s timeline. The current timeline, Tc , is then recommended timeline Tr ec are generated in L iterations.
extracted from the user’s timeline; it consists of the subsequence
d(Tj , Tc ) − min d(Tp , Tc )
of the N activity objects occurring prior to aoc and ends with aoc Tp ∈T
(Step 1). Score(ao) = 1 − . (1)
max d(Tp , Tc ) − min d(Tp , Tc )
The recommendation of each activity object aor ec i in Tr ec is Tp ∈T Tp ∈T
performed iteratively (Step 4) as follows (see [16] for details). For
each previous occurrence in the user’s timeline of an activity with 2.2.1 Distance between Timelines. For the purpose of determin-
the same name as aoc (e.g. ‘Italian Food’), a candidate timeline (Tj ) ing the similarity between two timelines T1 and T2 , the two-level
is extracted (Step 5). Let T be the set of all candidate similarity algorithm proposed in our earlier work [15] is used. This
timelines in algorithm first computes the minimum cost of rearranging the ac-
a given iteration. A two-level edit distance d(. , .) between each
candidate and the current timeline is computed [15]; based on these tivities to achieve the same activity sequence and then aligns the
distances, a score (Eqn. 1) is assigned to the activity that occurs values of the features of the corresponding activity objects. See [15]
immediately after each candidate timeline Tj in T (Steps 7–8). for further details on this approach.
RecTour 2017, August 27th, 2017, Como, Italy. 27 Copyright held by the author(s).
2.2.2 N-count matching. The matching unit determines the length
of the subsequences to be considered when calculating the distances 9 30
between timelines. The SeqNCSeqRec algorithm uses the N -count
Median of % agreement
8 Algorithm Algorithm
25
matching approach as proposed in [16]. Thus, the N activity ob- 7 SeqNCSeqRec SeqNCSeqRec
20
over users
jects in the timeline preceding the current activity object form the 6 BiGramSeqRec BiGramSeqRec
current timeline (and likewise for candidate timelines). Note that 5 PopSeqRec 15 PopSeqRec
the optimal value of N for each user will differ, depending on the 4
degree of repetition and regularity of activities performed by each. 3 10
2
5
1
3 EVALUATION
0 0
We first describe the dataset used to construct activity timelines 1 2 3 1 2 3
for users and the experimental methodology employed. This is Sequence length (k ) Sequence length (k )
followed by an evaluation of the proposed N -count based sequence (a) (b)
recommender.
Figure 1: Median percentage agreements for recommended
3.1 Dataset sequences for SeqNCSeqRec and baseline algorithms using
For our experiments, we used a subset of the Gowalla checkins timelines constructed from categories at (a) level 2 and (b)
dataset [21]. The complete dataset obtained contains around 36 level 1 in the hierarchy.
million checkins, 2.8 million locations and 0.3 million users. Every
checkin is bound to a specific location and timestamp. A subset of
sequence of categories of length 3 was generated at different recom-
these locations have categories assigned to them, such as, ‘Italian
mendation times (RT s), which corresponded to the end time of each
Food’, ‘Bookstore’, ‘City Park’, etc. These locations also have contex-
activity object in the test timeline. Recommendation performance is
tual features such as latitude, longitude, number of users checking
evaluated using agreement @ k (k = 1, 2, 3) which is the percentage
in to it, number of photos taken at the location, etc. In relation to
of RT s for a user where the first k categories in the recommended
our recommendation framework, each of the location categories is
sequence and the actual sequence are an exact match.
considered as an ‘activity name’ and the recommendations made
For the computation of two-level edit distances between time-
will be sequences of these categories. Hence, for evaluation, we
lines, the following operation costs and feature weights were used:
select only those checkins locations which have assigned categories.
c ins = cdel = 1, and c sub = 2 ; wcat eдory = 2, w st ar t −t ime = 1,
Further, categories are organised in a three-level hierarchy, con-
wpopul ar ity = 1, wlocat ion = 1. These weights were set according
sisting of 7, 134 and 151 level 1, 2, and 3 categories, respectively. For
to their hypothesised importance from the perspective of compar-
example, the level 1 category ‘Food’ has child categories ‘African’,
ing timelines; for example, the weight associated with updating
‘American’, ‘Asian’, ‘Coffee Shop’, etc. at level 2, while ‘Coffee Shop’
the category was set to the highest value since this is clearly a key
has child categories ‘Starbucks’ and ‘Dunkin Donuts’ at level 3.
consideration when computing distances between timelines. See
Given our objective is to recommend activities (categories) to users,
[15] for details on the two-level edit distance approach.
we consider level 2 categories as the most suitable level of gran-
ularity, and hence any checkin locations with level 3 categories
are assigned the parent category at level 2. As such, the names of
3.3 Recommendation Performance
activity objects in user timelines are given by the level 2 categories The performance of our proposed sequence-based N -count se-
of the locations checked in to by users. quence recommendation algorithm (SeqNCSeqRec) is compared
Since the characteristics of the timelines on weekdays and week- to the following baselines:
ends are different, here we considered data corresponding to week- • The bi-gram-based sequence recommender (BiGramSeqRec)
days only. To address multiple consecutive checkins by users at the is based on the Markov assumption that the next activity
same location, we merged such checkins for a given user if they had depends only on the current activity. For each user, the fre-
the same category, were less than 600 meters apart and occurred quency of occurrence of all activity name (category) bi-grams
within an interval of 10 minutes. Further, we selected only those in the user’s timeline are computed. For a given RT , a se-
users which have checkin data for at least 50 days with a minimum quence of activity objects is recommended iteratively as
of 10 checkins per day. The sampled dataset had 916 users with 2.7 per SeqNCSeqRec except that, at each iteration, the most
million checkins in total. The median number of checkins per day frequently occurring bi-gram beginning with the current
for users varied from 11–134, while the median number of distinct activity name is identified, and the recommended activity
categories of checkins per day for users varied from 4–58. is simply that of the second element of this bi-gram. Such
Markov-based approaches have proved to be quite successful
3.2 Methodology in modelling sequences in previous studies [9].
An offline evaluation was conducted for the proposed recommenda- • For a given user and RT , at each iteration of the algorithm,
tion approach. Each user’s complete timeline was split into training the popularity-based sequence approach (PopSeqRec) recom-
and test timelines, where the test timeline contained data for the mends the activity that the user performed most frequently
most recent 20% of available days. For each user, a recommended at that time in the past.
RecTour 2017, August 27th, 2017, Como, Italy. 28 Copyright held by the author(s).
3.3.1 Algorithm Performance. Figure 1(a) shows the median per-
centage agreements (k = 1, 2, 3) over all users for the proposed Se-
Mean % agreement
2.5
qNCSeqRec recommender and the two baselines. For SeqNCSeqRec,
the results shown correspond to the optimal value of N -count for Group1[0]
each user. It is clear from these results that the proposed approach 2.0 Group2[1,4]
significantly outperforms the baseline approaches. For example, Group3[5+)
SeqNCSeqRec improves upon BiGramSeqRec by 16.98%, 45.38%, and
1.5
129.3% for recommended sequences of length 1, 2, and 3, respec-
tively, and improves upon PopSeqRec by more than 100% in all
cases. Differences in results between the proposed and baselines
0 1 2 3 4 6 8 10 12
algorithms are statistically significant (Wilcoxon-Mann-Whitney N−count
rank sum test) at the p<.05 level. The results also indicate that
performance declines when larger sequences are recommended, Figure 2: Mean percentage agreement for recommended se-
which is to be expected, given the increased challenges involved in quences over users in each group.
making such recommendations.
While the above findings are promising, it can be seen that the
percentage agreements achieved by all algorithms are relatively
low; for example, the percentage agreement is 9.5% for sequence
current activity only (N -count = 0); Group 2: next activities are
lengths of 1 using SeqNCountSeqRec. We make the following obser-
based on the current activity and a small number of past activities
vations in this regard. Firstly, as described in the previous section,
(N -count lies in the interval [1,4]); and Group 3: next activities are
in order to generate a sequence of recommendations, only the top-1
based on the current activity and a larger number of past activities
recommended activity is considered at each iteration of the SeqNC-
(N -count = 5+).
SeqRec algorithm. In addition, the evaluation is based on only a
In this experiment, users were assigned to one of the above
single recommended sequence being made to users, which clearly
groups based on the range in which their optimal N -count value
represents a strict approach.
appears (optimal in the sense that best percentage agreement was
Secondly, while many (although not all) level 2 categories are
seen for sequences of length 3). Overall, 421, 374 and 121 users
semantically similar, they are not considered a match according to
were assigned to Groups 1, 2 and 3, respectively. Results are shown
the evaluation metric. For example, consider the level 2 categories
in Figure 2. It can be seen that the mean recommendation perfor-
‘Mexican’ and ‘South American/Latin’ which relate to dining and
mance for Group 1 users (46% of all users) was significantly lower
are children of the level 1 category ‘Food’. From a recommenda-
than that seen for users in the other groups. This finding is to be
tion perspective, these different types of dining experiences are
expected, since it indicates that it is easier to recommend sequences
clearly related and (arguably) should represent a match. Thus, we
of activities to users which are more consistent in their activity
also evaluate our recommender when all checkin locations are
patterns. Thus, it can be concluded that adopting a personalised
mapped to level 1 categories in the hierarchy – i.e. user timelines
approach for users, by selecting the optimal N -count value for each
are constructed from activity objects with names given by the level
user, is important. While it is not feasible to determine this value by
1 categories of locations checked in to by users. The results are
experiment for large user bases, an approach to automatically learn
shown in Figure 1(b). While similar trends as before are seen, the
a suitable value for individual users such as proposed in previous
percentage agreements achieved are much greater; for example,
work [16] can be applied.
over 30% for SeqNCSeqRec compared to the previous 9.5% for se-
quences of length 1. The ‘true’ performance of the recommender
lies somewhere in between these values (since not all level 2 cate- 4 CONCLUSIONS AND FUTURE WORK
gories are semantically related); a further analysis of this matter is In this paper, we have expanded on our previous work to suggest
left to future work. sequences of activities for users based on past activity patterns.
Notwithstanding the strict evaluation metric used in this work, the
3.3.2 Performance across Users. A key intuition behind our ap- proposed approach shows promising performance and outperforms
proach is that the next activities performed by individual users the baseline algorithms considered. In future work, we will inves-
depends, to a lesser or greater extent, on their past activity pat- tigate collaborative approaches in which candidate timelines will
terns. In the proposed SeqNCSeqRec recommendation algorithm, be drawn from the activities of other users in the system. Further,
the number of past activities to be considered when generating rec- we will consider new approaches to suggest sequences of activities
ommendations is determined by the N -count value (see Section 2.2). (for example, using RNNs) and investigate the recommendation
In previous work [16], where the task was to recommend a single of context (for example, where, when, with whom etc.) associated
activity to users, it was seen that the optimal N -count value varied with each of the suggested sequence of activities.
across users. In this section, we investigate whether a similar affect
is seen when recommending sequences of activities to users.
As per [16], we hypothesise three distinct groups of users to 5 ACKNOWLEDGMENTS
capture the degree to which past activity patterns reflect future The Insight Centre for Data Analytics is supported by Science
activity performance – Group 1: next activities are based on the Foundation Ireland under Grant Number SFI/12/RC/2289.
RecTour 2017, August 27th, 2017, Como, Italy. 29 Copyright held by the author(s).
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