=Paper=
{{Paper
|id=None
|storemode=property
|title=Inferring contextual user profiles - improving recommender performance
|pdfUrl=https://ceur-ws.org/Vol-791/paper7.pdf
|volume=Vol-791
}}
==Inferring contextual user profiles - improving recommender performance==
https://ceur-ws.org/Vol-791/paper7.pdf
Inferring Contextual User Profiles - Improving
Recommender Performance
Alan Said Ernesto W. De Luca Sahin Albayrak
TU Berlin TU Berlin TU Berlin
DAI Lab DAI Lab DAI Lab
alan.said@dai-lab.de ernesto.deluca@dai-lab.de sahin.albayrak@dai-lab.de
ABSTRACT are commonly categorized as either model-based or memory-
In this paper we present the concept of inferred contextual based [8]. In this work we focus on the latter, which cre-
user profiles (CUPs) which extends the traditional user pro- ates item prediction for a user by finding users similar to
file definition by describing the user in a given situation, or that user (in terms of co-rated items), a so-called neighbor-
context. The approach is evaluated in the scope of movie hood. The information from the neighborhood is then used
recommendation. In our evaluation, we infer two CUPs for to predict items not rated by the user which should be of
each user, and use only one of the profiles, instead of the interest. Memory-based, or neighborhood-based approaches
full user profile for recommending movies. We evaluate the commonly use measures such as the Pearson correlation Co-
model on a data snapshot from the Moviepilot movie rec- efficient or cosine similarity to create the neighborhoods [14].
ommendation website, with results showing a substantial
improvement in terms of precision, recall and mean average However, in some situations, approaches using only the his-
precision. torical usage information of users are not capable of iden-
tifying relevant items [2], or approaches utilizing other in-
formation can provide better recommendations. Instead, if
Categories and Subject Descriptors at first identifying the situation, the context, a system can
H.3.3 [Information Search and Retrieval]: Retrieval provide tailored recommendations for the specific context,
models; H.3.5 [Online Information Services]: Web-based provided information about it is available.
services
In order to create a context-aware recommendation model,
General Terms one needs to define the concept of context. In this work we
Algorithms, Design, Experimentation, Human Factors use Dey’s widely-accepted definition: ”Context is any infor-
mation that can be used to characterize the situation of an
entity” [11]. Here, the entity is understood as an item which
Keywords can be influenced by contextual parameters that describe
recommender systems, collaborative filtering, experimen- the state of the user and item during consumption.
tation, context-awareness, user modeling, information re-
trieval, human factors, movie recommendation Context-aware systems commonly use a predefined static set
of contexts in order to generate recommendations for the
1. INTRODUCTION specific situation, e.g. weekday, season, time of day [4, 13].
Recommender systems have become a popular component
in online services to help and guide users in information We propose an approach for automatic context-inference in
retrieval oriented tasks [16]. Frequently, recommender sys- the scope of movie recommendation, based on the time of
tems infer the preferences of users based on a priori data, i.e. a rating event and the information on whether or not the
the already consumed data. Collaborative Filtering (CF) rated movie is still shown in the cinema.
models are the de facto standard in when it comes to rec-
ommendation of frequently consumed items, e.g. movies, Our approach to context-inference for recommendation is
books, etc [14, 16]. CF calculates the relevance of an item evaluated using a dataset from the Moviepilot1 movie rec-
for a user based on other users’ rating information on items ommendation website. We present an inferred Contextual
co-rated by the user and his or her peers. CF approaches User Model (CUP), a user profile, similar to the “micro-
profile” concept by Baltrunas and Amatriain [4]. Our model
infers the context of where a movie was seen (at the cinema,
or at home) through a combination of movie meta data, the
dates of when a movie was shown in the cinema, and the cre-
ation time of the rating, i.e. the time when the movie was
rated by a user. The model creates two “virtual” (context)
profiles for each user (two CUPs), the cinema CUP and the
CARS-2011, October 23, 2011, Chicago, Illinois, USA.
home CUP.
Copyright is held by the author/owner(s). 1
http://www.moviepilot.de
The biggest difference between our work and the related allows a flexible and generic integration of contextual infor-
work described in section 2 is that we infer Contextual mation using a User-Item-Context N-dimensional tensor for
User Profiles automatically (i.e. split users into context- modeling data, instead of the traditional User-Item matrix.
aware sub-profiles, as shown in Figure 1), and show that even In their “Multiverse Recommendation” model, every differ-
this simple model of context-inference adds to the quality ent type of context is considered as an additional dimension
of a recommender. The process is presented in detail in in the data representation, extending the user-item matrix
Section 3. to a tensor. The factorization of this tensor leads to a com-
pact data model that can be used to provide context-aware
Our experiments show that when using our context model, recommendations.
we can improve recommendation results significantly com-
pared to the uncontextualized preferences of users. The full Bogers [6], presents a movie recommendation algorithm,
details of our evaluation and results are presented in Sec- ContextWalk, based on taking random walks on the con-
tion 4. The paper is concluded by a summary of the contri- textual graph. In addition to the common CF user-item re-
butions and a discussion about future work in Section 5. lations, this algorithm allows the inclusion of different types
of contextual features, such as actors, genres, directors, etc.
Our main contribution is showing that a relatively simple It supports other recommendation tasks with the same ran-
inference model based on surrounding information can be dom walk model without the need for alteration or retrain-
used to boost recommendation results considerably. ing, e.g. recommending interesting movies or actors for a
specific group of users.
2. RELATED WORK
At the moment, recommender systems tend to use very sim- Contextual user modeling, and context-awareness in general
plistic user models, adding new user preferences to the exist- have been hot topics during recent years with numerous pa-
ing profiles as the users interact with more items (e.g. rate pers [4, 13, 17], workshops [3, 10], etc. covering the field.
new movies, buy new books, etc.). But these approaches of- However, the topic is not new, and has been touched upon
ten ignore the ”situated action” of the user. Situated action for the better part of the last 20 years. One of the earliest
states that users who interact with a system in a particular systems using the concept of location-based context, the Ac-
context have items that are relevant within that context may tive Badge Location System by Want et al. [18], introduced
find the same items irrelevant in a different context [15]. this type of context-awareness as a means of providing ser-
vices to people in an office environment. Similar systems
As stated by Mobasher [15], context plays an important role have been subsequently put to use both in research and the
in psychology for human memory as well as in linguistics industry, Bokun and Zielinski [7] for instance, created the
for disambiguation purposes. Research in intelligent infor- Next Generation Active Badge System which broadcast the
mation systems has also shown that incorporating context, location of the badge wearers. Abowd et al. [1] wrote about
or situational awareness, in the recommendation process in- context for mobile environments in the form of location for
creases the performance and perceived usefulness of recom- automated tour guides already in 1997.
mender systems [4].
Adomavicius and Tuzhilin [2] divide context-aware recom- 3. CONTEXTUAL USER MODELING
mender systems (CARS) into three types: Given an analysis of user modeling in the scope of recom-
mender systems, in this paper, we choose to extend the
term to contextual user modeling as our focus is on defining
1. Contextual Pre-Filtering, where context directs data context-aware user profiles (CUPs). Each CUP is specific
selection for the situations a user encounters.
2. Contextual Post-Filtering, where context is used for
filtering recommendations computed by traditional ap- The context profile model we describe is based on the lo-
proaches. cation and time, the context (or “situated action” [15]), in
which a user watches a movie. Given a set of users, movies
3. Contextual Modeling, where context is directly inte- and ratings with timestamps of when the rating event oc-
grated into the model curred, we infer the context of the rating event. We define
two CUPs, home and cinema and assign each user’s movie
ratings to one of these as shown in Figure 1. Assignment of
Contextual pre-filtering can be achieved by using “micro-
ratings is based on the assumption that movies rated within
profiles” where a single user profile is split into several, pos-
two months of their cinema premiere date have been seen
sibly overlapping, contextual sub-profiles, each representing
in the cinema2 , we consequently assume movies rated at a
the user in one or several particular contexts [4]. Here, the
later point in time are assumed to have been seen at home.
recommendation process uses these micro-profiles, not only
a single user model. The performance is shown to be better
Having created two CUPs for every user, we can now use
than that of traditional Collaborative Filtering methods.
a collaborative filtering approach to recommend movies for
Contextual post-filtering is applied within traditional ap- 2
proaches, while contextual modeling directly involves the the specific time a movie is shown in the cinema usually
varies depending on the number of visitors, however the time
model, e.g. adapting a generic tensor factorization approach. between the cinema and home release of a movie usually
An example of this is the tensor factorization-based Collab- varies between 4 weeks - 4 months [9], 2 months being typical
orative Filtering method, by Karatzoglou et al. [13], which for German cinema
each of the CUPs based on the ratings in each specific con- of movies seen by its users. One of the services offered by
text. Moviepilot are movie recommendations. Each user is pre-
sented with a set of movies which should be of interest.
ui uj uk um ul These recommendations are based on the users’, and their
ui uj uk um ul
home cinema home cinema home cinema cinema home peers’, previously rated movies.
ma 1 3 5 ma 1 3 5
mb 4 4 mb 4 4
This dataset is a subset of the full, unfiltered, data that cre-
mc 5 2 mc 5 2
md md
ates the basis for the Moviepilot website. The dataset was
5 3 3 5 3 3
me 3 4 1 1 me 3 4 1 1
obtained directly from Moviepilot, thus eliminating any in-
consistencies which might be the result of crawling a website
(a) Uncontextualized (b) The same rating matrix, like this. The dataset contains ratings by 10, 000 randomly
rating matrix. where users from (a) have been
divided into CUPs. selected users who have rated at least one movie. In addi-
tion to the ratings, the dataset also contains information on
when movies had their cinema premieres. The total num-
Figure 1: Shown is an example of a user-movie
ber of ratings in our subset is 1, 539, 393 spread over four
matrix (a) and a user-movie-context (b) matrix.
years. The total number of ratings in Moviepilot over the
Columns with identifier ui...l refer to users and rows
same amount of time is more than 7 million. Figure 2 shows
with identifiers ma...e to movies. The elements of the
the number of ratings per month in both datasets. The rat-
matrix are the ratings of users given to movies. All
ings are stored on a 0 to 100 scale with 0 being the lowest
users might only have one CUP, as is the case with
and 100 being the highest. The scale the users are presented
uk .
with is 0.0 to 10.0.
This type of modeling is in agreement with the pre-filtered
context-awareness concept discussed in Section 2. It is
4.2 Experimental Setup
also related to the time-based “micro-profiles” approach pre- The algorithm used to produce the recommendations is
sented by Baltrunas and Amatriain [4] where users are based on collaborative filtering [16]. We evaluate our results
also divided into sub-profiles, however these sub-profiles are on a subset of 10, 000 randomly selected users due to the
based on the time of the event only, without taking its loca- long running times of the experiments when the full dataset
tion and item specific meta data into consideration. was used. Even for this subset, each experiment took circa
3 hours to complete on a 2.4GHz dual core PC.
The rationale for this division is the assumption that people
have different rating profiles, or different tastes, based on For the experiments, 50 training and evaluation sets each for
where and when they see a movie, consequently the movies the original and for the contextual user profiles were created.
which should be recommended to users should be different The evaluation sets consisted of circa 5000 ratings for 500
depending on how the movie will be consumed. randomly selected CUPs for the contextualized evaluation.
Analysing the 10, 000 users in our dataset, we were able to
Our model is built upon the assumption that users rate identify 7, 487 cinema CUPs and 4, 670 home CUPs - mean-
movies they have seen within a short amount of time from ing that not all users seem to rate movies in both contexts.
the time of viewing, i.e. generally not saving up ratings for, For the uncontextualized case, the CUPs were merged into
rather rating them continuously . This is supported by the the original user, meaning a fewer number of columns in the
general rating trend shown in Figure 2. The graph shows the input matrix (see Figure 1(a)). The merged columns have
average number of ratings per user from the initial month of roughly twice as many ratings each though3 .
registration for both the subset used in our experiments (in-
troduced in Section 4.1) and the full dataset. As some users In order to avoid problems related to cold start, for both
stop using the service, the number decreases over time. The users and items, we decided that users in the evaluation sets
high amount of ratings in the beginning indicates that users had to have rated at least 30 movies. For each of these
rate a “larger than normal” amount of movies just after reg- users, 10 movies having been rated with a value above the
istration, in order to create their profiles, but after one or user’s average rating were extracted into the evaluation set
two initial rating sessions, the average number of ratings per (i.e. the set of True Positive recommendations). The rest
user per month stabilizes at between 10 and 12. There are of the ratings were used for training. The recommendation
no extreme anomalies (peaks) in the curve, would there be algorithm was run one time each for the 50 pairs of original
any, these would indicate accumulated rating sessions. and CUP datasets. The results presented in this paper are
averaged over all 50 runs.
4. EXPERIMENTS AND RESULTS The recommendation algorithm used in our experiments was
We evaluated our contextual user profile model on a K-Nearest Neighbor using the Pearson Correlation Coeffi-
dataset from the German movie recommendation commu- cient as the neighbor similarity measure. Experiments were
nity Moviepilot. It should be noted that the algorithm itself performed for K = 150. We evaluate our recommendations
is not the focus of our evaluation, rather the concept of in- with the Mean Average Precision (MAP), Precision at N,
ferred contextual user profiles. and Recall at N measures. These measures where chosen
since they are well-known and widely-used in the field of
4.1 Dataset 3
which should bias the results positively for the original
The Moviepilot website contains information and news setup as the number of true positives becomes twice as high
about movies, actors, directors, etc., as well as the ratings (at most) for the merged users compared to the CUP’s.
140
Subset used
Full dataset
120
100
# of ratings per user
80
60
40
20
0
0 12 # of months after first rating 24 36
Figure 2: The sum of the total number of ratings per month per user since their first rating. The number of
ratings, in both the full dataset as well as in our subset stabilizes at around 10 ratings per month per user.
The high number for the first month in our dataset is explained by the users in our dataset being active
users, i.e. who create a profile for the purpose of returning. The significantly lower value in the full dataset
is due to users who create a profile, rate very few items and never return.
0,018
Recommender Systems and Information Retrieval, provid- 288% Original Profiles
0,016 CUPs
ing a statistically sound estimate of the recommendation 186% 138% CUPs Home
quality [12]. 0,014
187%
CUPs Cinema
206% 204% 103%
0,012 165% 165% 146% 145% 100%
99%
4.3 Results 100% 99% 87% 86%
Precision
0,01
Figure 3 shows the precision levels obtained in our experi- 100%
0,008
ments. The recommendations using the contextualized user 100%
100%
profiles outperform the original dataset by 200% when rec- 0,006
ommending one item only in terms of average precision. The 0,004
approach consistently outperforms the baseline until the rec- 0,002
ommended set reaches circa 50 items. In terms of recall,
0
shown in Figure 4, the CUP approach consistently outper- 1 5 10 50 100
forms the baseline. When looking at each CUP separately N
we see that the home CUP outperforms all other approaches
(contextual and not contextual) by even more. The per- Figure 3: Precision@N with N={1, 5, 10, 50, 100}
formance in terms of recall is similar, however the original for the original user profiles, the average value for
users profiles never seem to be able to outperform the CUPs. both home and cinema CUPs and for each of the
When looking at MAP, shown in Table 1, the improvement two inferred CUPs.
is somewhat smaller, which is expected given the fact that
precision is higher for the original user profiles at high N’s.
able to considerably improve recommendation results in
The observed results confirm the assumption that the lo- terms of precision, recall and mean average precision. Re-
cation and situation (“situated action” [15]) influences the sults indicate that automatic contextualization of user pro-
consumer in such a way that the taste (i.e. rating value) dif- files into CUPs affects the quality of recommendations pos-
fers from situation to situation. This confirms the notion of itively. We showed that, in a movie recommendation sce-
users having separate rating profiles depending on the com- nario, the venue and time of a consumption as well as the
bination of where, how and when the movie is seen. More “freshness” of the item is reflected in the rating behavior of
importantly, the performance of a recommender system can users and that this information can be used for recommen-
be improved considerably if this information is used. dation purposes.
5. CONCLUSION The situation in which users consume a particular product,
In this paper we presented a method for automatic con- has an effect on their taste or rating behavior. However,
textualization of rating events in a movie recommendation the context covered in this work needs to be extended and
scenario, in order to create contextual user profiles, CUPs. further researched to gain more insight into the way contex-
By using the date of the rating, and the information on how tualized user profiles should be inferred, managed and used.
new a movie was at the time of rating, we were able to infer For instance, the profiles explored in this work are mutually
the venue (at home, or at the cinema) in which a movie was exclusive, which, in the presented recommendation scenario,
seen. seems plausible, as the location of an event can only be sin-
gular. If the context profile would be extended to include
We evaluated the inferred contextual user profiles and were factors such as company, mood or ambiance of the venue,
3,00E-02
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The authors would like to express their gratitude to the
Moviepilot team who contributed to this work with dataset,
relevant insights and support.
The work in this paper was conducted in the scope of the
KMulE project which was sponsored by the German Federal
Ministry of Economics and Technology (BMWi).