=Paper=
{{Paper
|id=Vol-2578/DARLIAP2
|storemode=property
|title=Culture-aware Point-of-Interest Category
Completion in a Global Location-Based Social Network Database without
Access to User Data
|pdfUrl=https://ceur-ws.org/Vol-2578/DARLIAP2.pdf
|volume=Vol-2578
|authors=Nikolaos Lagos,Ioan Calapodescu
|dblpUrl=https://dblp.org/rec/conf/edbt/LagosC20
}}
==Culture-aware Point-of-Interest Category
Completion in a Global Location-Based Social Network Database without
Access to User Data==
https://ceur-ws.org/Vol-2578/DARLIAP2.pdf
Culture-aware Point-of-Interest Category Completion in a
Global Location-Based Social Network Database without
Access to User Data
Nikolaos Lagos Ioan Calapodescu
Naver Labs Europe Naver Labs Europe
Meylan, France Meylan, France
nikolaos.lagos@naverlabs.com ioan.calapodescu@naverlabs.com
ABSTRACT culture-aware POI category imputation. We formally de-
Point of Interest (POI) categories can facilitate a number of ser- fine the problem and present a corresponding analysis.
vices, such as location-based search and place recommendation. • Culture-related categorisation without requiring access to
However, such information can be incomplete and/or incorrect, user data, has not been proposed before. To achieve that,
especially in crowdsourcing environments. In the literature, au- we simulate user information, by replacing culture-related
tomatic category imputation has been suggested to tackle this training inputs in an appropriate manner, at inference
problem, showing that contextual information is vital for increas- time. For instance, the country where a POI is located can
ing the quality of such predictions. To this end, users’ check-in be one of the training inputs. We can replace at inference
data, and most particularly location and time of visit, is often time the values of this input with the nationality of the
used as the notion of context. In this work, we propose a method user.
that considers culture as a contextual parameter. Contrary to The rest of this paper is organised as follows. We review related
existing methods, our approach does not require access to user work in Section 2. We formally define the problem in Section 3
data. We illustrate the feasibility of our method by performing and describe our method in Section 4. Experiments are presented
experiments on data from Foursquare, a global location-based in Section 5. Section 6 includes the conclusions of this work.
social network.
1.1 Industrial context
1 INTRODUCTION Our company Naver, provides, among other things, location-
based services. Good quality POI data is thus of major importance.
Point of Interest (POI) categories can facilitate a number of ser-
In this context, in Naver Labs Europe, we have been exploring
vices, such as location-based search and place recommendation.
automatic multi-lingual methods for completing and correcting
However, as discussed in recent work, categories, especially in
POI semantic tags found in Foursquare’s database, a global crowd-
crowdsourcing environments, can be incomplete and/or incor-
sourced location-based social network1 . The scope of our work
rect. Automatic category prediction has therefore been proposed
is to eventually support:
to remedy this problem and impute missing categories [20].
Recent advances in POI categorisation have shown that con- • A user that is not familiar with local culture to discover
textual information is vital for increasing the quality of automatic appropriate POIs in the vicinity of her/his position. If POIs
POI category prediction [5, 11, 15, 16]. To this end, users’ check- are not categorised appropriately, the user can not easily
in information, and most particularly location and time of visit search for them and they will not be included in the search
are often used to define the context. There are two important results. In addition, proper POI categorisation could also
shortcomings though with these approaches: help in recommendation.
(1) Getting relevant data presupposes that users’ will allow
their check-in data to be shared with the corresponding 2 RELATED WORK
service. However, recent initiatives and laws, such as the 2.1 Point-of-Interest Category Prediction
EU General Data Protection Regulation (GDPR), stipulate Most of the work on Point-of-Interest category prediction has
that users should have more control over their personal taken place in the context of Location-Based Social Networks.
data, and potentially disallow the use (and storage) of data, Ye et al. [16] were the first to show that taking into account
such as their check-in information, from third-parties. the geographical, local context of users can improve POI recom-
(2) Context is defined in terms of location proximity in exist- mendation and categorisation. From then on, other related work
ing systems. However, recent advances in recommender has been systematically using such contextual data, originating
systems and information search have shown that integrat- from check-ins and/or related mobile sensors [15].
ing information about cultural backgrounds, especially Krumm [11] showed that other personal information (e.g. gen-
in a global setting, can help in developing high quality der and age range) could also help to improve the results further.
systems [7, 17]. The type of user data explored in the state-of-the-art include
The main contributions of this work are as follows. the number, frequency, time and duration of user check-ins, and
• To our knowledge this is the first study of a multi-lingual, sometimes demographic information (e.g. age range, gender).
global location-based social network database related to This data is usually combined with the location of the POI and
its proximity to other POIs.
© 2020 Copyright for this paper by its author(s). Published in the Workshop
Proceedings of the EDBT/ICDT 2020 Joint Conference (March 30-April 2, 2020,
Copenhagen, Denmark) on CEUR-WS.org. Use permitted under Creative 1 We got access to this data thanks to an agreement between Naver Labs and
Commons License Attribution 4.0 International (CC BY 4.0). Foursquare.
He et al. [5] and Zhou et al. [20], have in addition used POI in- In this work, we assume that there are several "culture spe-
n
formation including user defined semantic tags, and name token cific" labelsets, such that
S
L j ⊆ L, where n is the maximum
embeddings pre-computed on a domain-specific corpus. j=1
Jiang et al. [8] apply machine classification techniques to the number of cultures that are represented by the labels in Λ and
problem of fusing different POI databases under a common clas- n < ∞. For instance revisiting our example, if we assume that
sification hierarchy, the North American Industry Classification L = {Japanese Restaurant, Noodle House, Ramen Restaurant },
System (NAICS). Their study involves only a few American towns and we have at least two cultures French and Japanese with
and they use as input features only the categories and their pre- corresponding labelsets L 1 and L 2 , then it could be that L 1 =
defined relations, as already manually attributed in the original {Japanese Restaurant, Noodle House} and L 2 = {Noodle House,
data sources to the POIs. Ramen Restaurant }, while Lo = {Japanese Restaurant }.
A number of works have been carried out on location predic- We denote by y = (y 1 , ..., ym ) an m-dimensional binary vector
tion based in social streams, e.g. Twitter, where the main research where y i ∈ [0, 1] such that y i = 1 if and only if li ∈ L. We
interest is using noisy and short text for classification. For in- define a variant of it for culture c as yc = (yc1 , ..., ycm ) where
stance, Cano et al. [2] uses tweets to infer volatile POI classes yci ∈ [0, 1] such that yci = 1 if and only if li ∈ Lc and each y i − yci
according to specific temporary events happening at a specific is non-negative. Accordingly, the m-dimensional binary vector
location. Interested readers may refer to Zheng et al. [19] for a yo = (yo1 , ..., yom ) with y i ∈ [0, 1] has yoi = 1 if and only if li ∈ Lo .
comprehensive survey of the domain. Despite superficial com- We assume that C ∪ N = A where C stands for the set of
monalities, this subject is different from the one studied here. culture-related attributes, and N the set of culture-independent
To summarise, as mentioned in the Introduction, all the afore- ones. Considering again our example, the C could be instantiated
mentioned approaches assume access to user data at training by the country where the restaurant is located, and N by the
time and have not dealt with the notion of user’s culture. name "La Table du Ramen".
We denote by x, xC , x N the vectors that represent correspond-
2.2 Culture-aware Recommendation ingly A, C, and N , such that the observed p in the dataset, is
Recent work had highlighted the importance of modelling users’ defined as
culture in recommendation and information search [7]. Notably, p = {x, yo } = {xC , x N , yo }
the cultural background of a user was found to play a vital role Our objective is to make culture-specific predictions such that
in how recommended items are judged [14]. for culture c
To our knowledge, in the area of automatic recommender p = {x, yc }
systems, Zangerle et al. [17] uses culture as a computation pa- We thus formulate our goal as a multi-label classification problem
rameter. The authors, base their proxy for defining culture on where we want to find a classifier bc : X → Y where X is the
the nationality of the users and use Hofstede et al’s. [6] grouping input space (all possible attribute vectors) and Y the output space
of nationalities in culturally similar clusters as a guide for their (all possible labelset vectors), such that yc = bc (x ).
computation model2 . However, in this case as well, the authors
assume that they have access to user data. 4 CATEGORY PREDICTION METHOD
The category of POIs, especially in a location-based social net-
3 PROBLEM DEFINITION work, is related to the cultural profile of the users that visit it.
Our goal is to predict POIs’ categories that are appropriate to a This has been proven via the inclusion of user profiles in related
specific culture. For instance, a typical place found in the database work (c.f. Section 2). However, instead of accessing user informa-
of Foursquare is "La Table du Ramen"3 , which is located in Paris, tion to discover such profiles, we use the observation that the
France. The category found in Foursquare’s database is Japanese majority of POIs are categorised in a manner that reflects local
Restaurant, which may be sufficient for the local culture. However, culture in location-based social network databases. For instance,
a Japanese would expect the POI to be categorised at a much as shown in Fig. 1, we find in Foursquare that restaurants selling
more fine-grained level, as e.g. Ramen Restaurant or as a Noodle noodle dishes4 are usually categorised as Asian Restaurant in
House. The objective is to automatically predict such categories, France, while Ramen Restaurant is by a large margin the most
according to one’s culture. popular category in Japan.
We consider that POI category prediction in our context is
equivalent to the problem of completing a specific attribute of
the dataset, the one that represents categories of POIs, based
on data from the remaining attributes. Formally, a POI p should
have an attribute that includes an ideal, complete and correct,
category labelset i.e. set of relevant labels L ⊂ Λ, where Λ =
l 1 , ..., lm is the set of all possible labels, while the rest of the
attributes are represented by the set A. However, in practice,
the set of labels attributed to p, what we call thereof the ob- Figure 1: Category distribution of POIs having the token
served labelset Lo ⊂ Λ, can be incomplete and/or incorrect. "noodle" in their name in Japan and France. It is obvious
that "Ramen Restaurant" is the most popular category in
2 Hofstede et al. [6] notes that culture is always a collective phenomenon, as it is, at Japan and "Asian Restaurant" in France.
least partly, shared with people who live or lived within the same social environment,
which is where it was learned. In that sense, context may encompass a lot of different
aspects, including the notions of social status, education, and language. Hofstede et Based on this insight, if at training time we use culture related
al. [6] mentions that "one’s country" is an important parameter that defines culture attributes to learn a latent representation of POI’s categories,
in this sense [6]
3 English translation:The Table of Ramen. 4 The token "noodle" is explicitly mentioned in the POI name.
then at inference time we can replace the corresponding inputs latitude and longitude are two of the most frequently used geo-
according to the target cultural profile. For instance, if at training graphical coordinates. The predominant way of modelling coor-
time we use the country in which the POI is located as an input dinates is to discretise the input space [13, 18]. This could take
parameter to our model, at inference time we can replace the the form of a grid separated into a fixed number of cells. Usually
value of this parameter with the target country, simulating what in this case the form and granularity of the cells has to be selected
would happen if the same POI was located in the target country appropriately. We use countries as a proxy of different cultures.
instead. We can thus generate culturally-appropriate predictions We represent countries as categorical variables, as this granular-
and complete the database offline. Revisiting the above example, ity can be related to different cultures, as explained in Section 2.2.
we can assume that some of the Ramen restaurants located in However, other representations could also be suitable, such as
Japan, would be categorised in a different manner, e.g. as Noodle regions etc.
Houses, if the value of the country was changed to France5 . An
overview of the proposed method is shown in Fig. 2. Note: Other cultural variables. This is an optional category,
Based on the discussion above, we reformulate our problem, which can include other parameters related to culture. For in-
and look for a classifier bc such that yc = bc (xc ) where xc is a stance, to determine socio-cultural context, opening hours and
culture-specific variant of the input. the price range of corresponding services may also be impor-
To find bc , we follow a standard approach and transform our tant. Both of these variables could be discretised and considered
problem into finding a real-valued vector function f : X → S ∈ categorical variables.
[0, 1]m that allows to indicate the relevance of a label li in rela-
tion to the input i.e. f (xc ) = ( f (xc , l 1 , ), f (xc , l 2 ), ..., f (xc , lm )) 4.2 Training
where f (xc , li ) is the confidence of li ∈ Λ being a correct label Once we vectorise our attributes, as explained in the previous
for xc and m is the number of labels. Actually this corresponds to section, we use a concatenation layer to combine them.
an estimation of p(yci |xc ) : yci ∈ [0, 1]. Note that ideally, observed If a is a POI attribute such that a ∈ A and ϕ a (x a ) ∈ ℜD a is the
outputs should be completely specified vectors, however in our attribute specific vectorisation function, where D a denotes the
context the training instances are only partially complete, so of dimensionality associated with the attribute a, then the final input
the form (xci , yoi ). We follow the Binary Relevance method, thus vector is a concatenation of all vectorised individual attributes:
learn m binary models, each specialised into predicting whether
one label is correct or not, independently from the other labels. x̃ = [ϕ 1 (a 1 ), ϕ 2 (a 2 ), ..., ϕ n (an )]
For an unseen xc , the predicted labels are then the union of the
predictions of all the binary models. where n denotes the number of attributes. We feed this to a dense
To learn the binary models we perform the following steps. layer
• Attribute selection: We use the name and spatial geo- h = relu[W h x̃ + b h ]
coordinates of the POIs. After applying a dropout layer, we then calculate
• Vectorisation: This step includes transforming the attributes
in a form that can be treated by the classifier, as explained p(y|h, θ ) = siдmoid[W h + b]
in Section 4.1.
where θ = (W , b,W h , b h ) are learned parameters of the model.
• Training: A model that learns to predict the probability of
siдmoid (s) denotes the logistic function f (si ) = 1+e1 si . The pa-
li being a correct label given x is computed in this step.
rameters θ are learned by minimising the binary cross-entropy
As explained in the previous paragraph, our problem is
loss function.
casted as a supervised machine learning problem. Details
are provided in Section 4.2.
• Inference: Whether li is a correct label for culture-specific
4.3 Inference
variants of x is computed in this step. Details are given in The multi-label model learned is given culture-specific inputs at
Section 4.3. this step. It then generates for each label a probability score. To
get from that the corresponding set of accepted labels, a constant
can be applied as threshold (usually this is 0.5) [4].
4.1 Vectorisation
Categorical variables. We represent them with one-hot en-
5 EXPERIMENTS
coded embeddings, as usually reported in the literature.
5.1 Set up
Sequential variables. Biessmann et al. [1] report that character- 5.1.1 Data. We perform our experiments on 2.4M POIs ex-
based representations are more robust for a similar setting to ours tracted from a large database provided by Foursquare to illustrate
(i.e. sparse data and multiple languages). In addition, Joulin et al. the feasibility of our method. Details are provided below.
[9] and Biessmann et al. [1] mention that character n-grams can
perform better than simple, unigram, character-based LSTMs. Categorisation hierarchy. Our dataset includes 808 POI cat-
After experimentation we have adopted trigram character based egories from the categorisation hierarchy of Foursquare6 , as
LSTMs for POI names. shown in Table 1. As the classification hierarchy is based on
crowdsourced data, the parts of the dataset that include more
Spatial variables. Geographical coordinates are the most im- POI instances are represented with more categories, resulting in
portant spatial attributes that characterise a POI. For instance, it being heavily imbalanced. For instance, the most well devel-
oped category that is located at the root of the hierarchy is Food,
5 In our experiments, 7% of them were categorised as "Noodle House" c.f. Section 5. 6 https://developer.foursquare.com/docs/resources/categories as of 3rd October 2018
Figure 2: Overview of proposed method. Images of NN models are adapted from [3]
Table 1: Category Distribution in the dataset Table 2: Percentage & distribution of semantic tags
Root Category Levels Categories in Path Category POIs (%)
Food 5 337 Café 9.83
Shop & Service 3 144 Restaurant 5.88
Outdoors & Recreation 4 83 Pizza Place 4.49
Professional & Other Places 3 77 Coffee Shop 4.48
Arts & Entertainment 3 53 Bakery 3.8
Travel & Transport 3 44 Fast Food
3.23
College & University 3 32 Restaurant
Nightlife Spot 3 25 ... ...
Event 2 8 Chinese Restaurant 2.98
Residence 2 5 Japanese Restaurant 2.5
Asian Restaurant 2.41
... ...
with 336 categories distributed in 5 levels. The least developed Noodle House 1.02
one is Residence having only 4 subcategories, over 2 levels7 . ... ...
Label distribution. We have extracted a sample dataset from the Ramen Restaurant 0.65
existing Foursquare database for our experiments. We used the ... ...
most well developed root category, Food, as seed, and took only
POIs with a high reality index. The distribution of the categories
is similar to the one found in the original hierarchy. To better longtitude are written in the standard form, normally with >10-
understand the dataset we provide the overall distribution of decimal point precision (e.g. latitude:55.76942424341726, longti-
POIs and the one of the top categories in Table 2. The dataset is tude: 44.948036880105064). Comments are also available for some
skewed in terms of the POI instances attributed to each category, of the POIs but as they are relatively sparse we chose not to use
with the first 10 top categories having more than 40% of the POIs them for the current experiments.
attributed to them. Dataset creation. To generate training, development, and test
Point-of-Interest attributes. . POI attributes include its name, data, we used approximate stratified sampling. The goal was to
and the latitude and longitude, transformed into the represen- maintain the distribution of positive and negative examples of
tation discussed in Section 4.1. It is important to note that con- each label by considering each label independently. Consequently,
trary to completely freely crowdsourced POI databases such as we allocated the POIs proportionally into 40% for training, 10%
OpenStreetMaps [12], the format is normalised. Latitude and for development, and 50% for testing purposes8 .
7 Even if we used Food as seed, we find also the rest of the root categories in the 8 We kept a relatively large percentage of the data for testing pusrpose, in order
dataset. The reason is that POIs can be categorised using multiple labels, although to have a large enough sample of POIs belonging to long tail categories, where
at least one of the labels must have as seed Food. cultural differences may be more obvious.
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global location-based social network, Foursquare, that give us
promising results. In future work, these results will be further
verified with user studies.
Table 3: Culture-specific prediction results for different POI categories. Green coloured values are
significantly higher than in the the rest of the cultures, and red significantly lower, indicating a notable
culture-specific influence.
Category Original data Culture
KR FR US BR TR GR
Acai House 1488 43 29 0 1534 0 0
Note:Except for Brazil in the rest of the cultures the same POIs are categorised as Snack Place, Juice Bar, Dessert Shop.
According to Foursquare’s documentation Acai house is a category only supported in Brazil.
Bistro 879 470 3310 351 1162 95 10
Note: Bistros predicted using the French culture are tagged in the original data as: Café, Bar, Gastropub, Diner. Corresponding
predictions in other cultures are: Café, Wine Bar, Bar, Gastropub.
Brasserie 18 0 91 0 3 0 0
Note: In other cultures Café is the main predicted category for the same POIs (or there is no prediction at all). In the silver
standard the POIs are also categorised as Bistro or Café.
Café 126665 108908 87381 63223 108837 148050 236436
Note: In the US culture a lot of Cafes seem to be categorised as Coffee Shops instead. In the Greek culture Coffee Shop,
Breakfast Place, Dessert Shop, Bar, Tea Room POIs are categorised as Café (which is actually representative of the culture).
Coffee Shop 54291 49069 45769 68102 50513 52742 23629
Note: As explained in the previous row.
Churrascaria 557 0 0 0 674 0 0
Note: Churrascaria is a Portuguese/Brazilian BBQ. In other cultures the majority of the same POIs are classified as BBQ Joint
and a small percentage as Steakhouse (especially in the US).
Creperie 978 775 2968 932 1138 917 1740
Note: Creperies are obviously common place in the French culture. In the US and KR ones the same POIs are rather
categorised as Dessert Shop or Breakfast Shop. In the BR one in addition to Dessert Shop we find also Pastelaria9 .
Dessert Shop 15164 19422 4747 9651 15460 21941 13736
Note: In the French culture Dessert Shop POIs are rather classified as Café, Bakery, Creperie, Pastry Shop, Chocolate Shop.
In the US one we have to note the large number of POIs categorised as Ice Cream Shop, Frozen Yogurt Shop, Candy Store.
Diner 2590 2289 2388 3980 1539 1593 100
Note: Some of the POIs categorised in the US culture as Diner, are mainly categorised in other cultures as Café or Breakfast
Spot or there is no prediction (the difference is really big with Greece where almost all of them are categorised as Café).
Friterie 656 7 4864 2 419 4 62
Note: The model has learned that Friterie is a culture-specific category (supported in FR, BE, NL in the Foursquare database).
It is interesting to note that in the US culture the same POIs are categorised as Burrito Place, Taco Place, Food Truck, in the
TR one as Kofte Place and in the GR one as Snack Place. They all seem to share a fast food aspect.
Meyhane 854 0 0 0 0 2157 0
Note: The model has learned that Meyhane is TR culture-specific category. In other cultures Meyane POIs usually do not get
any prediction.
Pastelaria 340 1 0 0 898 57 0
Note: The model has learned that Pastelaria is BR (and Portuguese) culture-specific category. In FR culture categorised as
Creperie, Snack Place, Bakery, US:Bakery,Snack Place, KR:Bakery, Dessert Shop, Snack Place.
Pastry Shop 60 5 307 0 0 88 0
Note: The model has learned that Pastry Shop is more frequent in FR (i.e. Patisserie). In other cultures we mainly find Dessert
Shop or Bakery.
Souvlaki Shop 94 0 0 0 0 0 2434
Note: The model learned that Souvlaki Shop is specific to GR culture. In other cultures and in the silver standard, the same
POIs are categorised mainly as Fried Chicken Joint and BBQ joint. There is a strong correlation to Steakhouse as well, or
more precisely to Kebab shops that are also classified as Steakhouse in the silver standard.
Sports Bar 67 9 2 100 4 5 0
Note: The model learned that Sports Bar is more frequent in the US culture. In the silver standard, the same POIs are
categorised also just as Bar and/or Wing Joint. Furthermore, the model learned a strong correlation between the category
Wings Joint and sports Bar - the two categories are predicted together 77% of the time.
Takoyaki 186 130 45 34 22 44 30
Place
Note: Takoyaki Place POIs do not get any prediction most of the times in other cultures except for KR. 86% of the predicted
POIs are correct according to the silver standard.