=Paper=
{{Paper
|id=Vol-1670/paper-45
|storemode=property
|title=Topical Video-On-Demand Recommendations based on Event Detection
|pdfUrl=https://ceur-ws.org/Vol-1670/paper-45.pdf
|volume=Vol-1670
|authors=Tobias Dörsch,Andreas Lommatzsch
|dblpUrl=https://dblp.org/rec/conf/lwa/DorschL16
}}
==Topical Video-On-Demand Recommendations based on Event Detection==
https://ceur-ws.org/Vol-1670/paper-45.pdf
Topical Video-On-Demand Recommendations based on
Event Detection
Tobias Dörsch, Andreas Lommatzsch, and Christian Rakow
DAI-Labor, TU Berlin, Ernst-Reuter-Platz 7, D-10587 Berlin, Germany
Abstract. Recommender systems help users to discover relevant items. Tradi-
tionally, recommender systems rely on both detailed knowledge of the domain
and an extensive user profile. However, small numbers of users, privacy con-
cerns, or a very specific domain limit access or availability to this information.
In this work, we present an approach for recommending items based on events
relevant to the target group of our system. We exemplify the approach with the
aid of a Video-On-Demand platform specialized in independent and art-house
movies. Our recommender analyzes domain-specific blogs and news. It extracts
current events that can be used for triggering topical recommendations. We
show that our approach successfully identifies relevant events and provides
highly relevant results without requiring detailed user profiles.
Keywords: recommender, event detection, privacy preserving recommender,
Linked Open Data, video on demand
1 Introduction
The rapidly growing amount of items in online shops and entertainment services
make it very hard for users to find relevant items. Recommender systems have been
developed for supporting users to discover items potentially unknown and matching
the user preferences. A widely used approach is user-based collaborative filtering
computing the similarity between users and suggesting items that users with similar
interests liked. A weakness of collaborative filtering is that current trends and tempo-
ral aspects are not taken into account. In many scenarios the context and seasonality
have a high influence on the user preferences. Traditionally, experts (e.g. “curators”)
compile topical recommendations in video shops or libraries taking into account
new releases, trends as well as current events. This motivates us to develop a recom-
mender that scans several different news streams, detects relevant events, and uses
this information for computing recommendations.
Video-on-Demand (VoD) systems allow users to watch almost any movie at any
time. The challenge for a VoD recommender system is not only identifying items
Copyright © 2016 by the paper’s authors. Copying permitted only for private and aca-
demic purposes. In: R. Krestel and E. Müller (Eds.): Proceedings of the LWDA 2016 Work-
shops: KDML, WM, IR, and DB. Potsdam, Germany, 12.-14. September 2016, published at
http://ceur-ws.org
matching the user preferences but also computing when to recommend an item. In the
past, curators knowing the typical seasonal user preferences and the relevant events
(awards ceremonies, holidays, etc.) created a schedule when to broadcast a movie. We
bring this principle to the VoD recommender. The recommender determines events
and trends relevant to a specific target group. Based on these events we compute
topical recommendations, which can be weighted by individual preferences. The
event-based recommendations are often helpful for escaping the filter bubble and for
suggesting items related to current trends.
We develop a recommender system for a VoD service focused on independent and
art-house movies. In contrast to main stream VoD services, our portal does not offer
blockbuster movies but a carefully selected catalog of films tailored to the needs of a
niche market. A remarkable fraction of the offered films are documentaries and films
related to current political topics. The requirements in the scenario are providing new
relevant recommendations every day without relying on user profiles. We build our
system on the idea that recognizing events relevant to our target group is a valuable
basis for recommending relevant movies.
The identification of events suitable for recommending items leads to several
challenges. To extract events, suitable sources must be identified and appropriate ways
of processing and storing the contained information must be developed. This task
requires learning algorithms able to identify events in streams of news data suitable
for recommending items (films). Dependent from the different events types, adequate
methods are needed for the event recognition and for linking events and films. In
addition, explanations for the suggested items should be provided for improving
the trust in the suggestions since recommendations based on news events are still
unfamiliar for most users.
The remaining paper is structured as follows. Sec. 2 summarizes related work and
discusses the connection to relevant research domains. Our approach is presented in
Sec. 3. In Sec. 4 we evaluate our approach and discuss the strengths and weaknesses of
our approach. Finally, a conclusion and an outlook to future work are given in Sec. 5.
2 Related Work
The task of recommending films on a daily basis is related to different domains.
CF-based Recommender Most movie recommender system focus on collaborative
filtering (CF) [4]. CF-based approaches analyze the ratings users assign to items. The
predictions are calculated by computing the similarity between either users (“user-
based CF”) or the similarity between items (“item-based CF”). A requirement for
getting high-quality recommendations is that a sufficient number of ratings for every
user and every item are available. Well-known problems of CF-based approaches are
the popularity bias [6] and the cold start problem [1]. CF-based algorithms tend to
suggest popular, often already known items.
Context and Event Detection Beside the individual user preferences several different
aspects influence the perceived relevance of movies, e.g. seasonality or the relation
to events. Studies analyzing the messages in social networks show that holidays and
recent events have a high impact on the discussed topics [2]. The detection of events
and the aggregation of messages related to the events are research topics in the
analysis of social networks and news streams. Hennig et al. [3] applied clustering
algorithms to news streams for identifying events in the news. The focus of the work
lies on extracting and tracking topics but not on recommending items. Macedo et
al. [5] developed a system that recommends social events. Based on the analysis of
the user’s past behavior, the proposed system recommends events based on social
distance, and both location and time preferences.
Discussion Contexts and events have a high impact on the interest of users. Hence,
building recommender systems computing recommendations based on relevant
events is a promising approach helping users to escape the filter bubble and to find
items related to the current topics of interest.
3 Approach
We develop a recommender system implementing a 4-layered architecture. The first
layer collects news from heterogeneous sources. The second layer aggregates the
collected data and extracts potentially relevant events. In addition, semantic data
collections are integrated in order to consider expert-defined events (such as birthdays
or memorial days). The third layer computes recommendations based on the events
relevant to the target group. In the 4th layer, the recommendations are enriched and
optimized for presentation. Explanations are generated for improving the trust in the
relevance of the recommendations. The architecture of the system is visualized in
Fig. 1. In the next paragraphs, we explain the implemented components in detail.
3.1 Collecting Data for Detecting Events
The crawlers continuously collect data being the basis for the identification of events.
In order to focus on the events relevant to the target group, we carefully select the
sources. In our scenario, we are especially interested in the domains art house, festi-
vals, and documentaries. We analyze the RSS feeds of portals reporting on the domains.
In addition, we crawl the T WITTER messages of an expert-defined set of accounts
(using the T WITTER streaming API). In addition, we collect tweets from the major news
portals for tracking the most relevant topics in the domain of politics. The selection of
sources grants us access to up-to-date knowledge from domain experts. These experts
typically write about the most relevant events and current trends. In our system we
monitor º 800 TWITTER accounts and º 15 RSS feeds.
3.2 Recognition of Relevant Events
We consider two types of events. “Static” events such as birthdays, anniversaries, and
memorial days are imported from semantic data collections. “Dynamic” events such
as won awards, politic events, or the death of a director are detected in the news
streams.
data sources extraction of relevant events computation of recommendations
person movie title peak Movie
Twitter detection detection detection Database
- titles
Crawler - descriptions
- meta-data visualization
recommen-
RSS dations
Reader find movies
related to the - newsletter
relevant events events/entities
and terms - front page
Event
Database
Fig. 1. The main components of our recommender system.
Knowledge Source for Events The “static” events are separated into two groups. The
first group is formed by person- and movie-related events. The second group is built
by holidays and memorial days typically related to specific keywords and genres.
Relevant Persons: Based on the movie catalog we know the persons related to the
potentially relevant movies. We link the persons with DB PEDIA in order to collect all
available birthdays of persons related to the movies. The same procedure is done
for movie release days and awards won by the movie. The challenge in the task is
the ambiguity of names and titles. We address this issue by computing a matching
score taking into account context data. We only connect persons with DB PEDIA if the
confidence score is above a threshold in order to prevent false positive matches. The
score is calculated using the attributes of the entities (occupation, age, synopsis). User
feedback is incorporated in order to correct and extend the automatically created
links.
Relevant Holidays: In contrast to persons directly listed in the meta-data describing
movies, the relations between holidays and movies are computed based on the textual
description of the holidays. For this purpose we search the name of the holiday in the
movie description and compute the textual similarity between the descriptions of the
holidays (retrieved from DB PEDIA) and the synopsis of the movie. If the relatedness is
above a threshold (optimized on a training dataset) the movie is linked to the holiday.
Discussion: Aggregating the different types of events, we find on average of about 20
events for each day of the year. This number of potentially relevant events allows us
to filter out the most relevant events taking into account feedback from users and
experts. In addition, the number of potentially relevant events allows us to ensure the
diversity of events (e.g. with respect to actors, directors, composers as well as birthday,
anniversaries). Static events are related for a specific day and typically recalled on the
date of occurrence. However, some users may still be interested in the event a few
days earlier or later (e.g. if they do not use the portal during the week). In order to
make these recommendations available to those users, the relevance of static events
degrades slowly over the course of 5 days.
Identifying Events in Tweets and RSS Feeds For recognizing events in news streams,
we analyze how often a relevant person (listed in the movie catalog) is mentioned in
the news or tweets on a daily basis. An event is detected if a person is much more
frequently mentioned than during an “average” day. Due to the large differences
in popularity of movies and people, we implemented a 3-dimensional model: The
popularity of a topic is identified by a long- and a short-term change in mentions
as well as by the number of sources in that the topic is recognized. Since we do not
compare topics against each other, each topic must fulfill criteria that are specific to
its own time series. This leads to higher diversity in the recommended movies as well
as to a broad spectrum of movies. In general, trend-detection for popular persons
works more reliably than for unpopular persons. This is due to the fact that an increase
of mentions of a popular topic is larger and thus easier to separate from noise.
Discussion The detection of the events and the linking of events with entities is the
central component of the recommender. The recognition of static events is computed
in advance when new movies are added to the catalog. The linking of dynamic events
is done on a daily basis. The process is based on several text mining and similarity
computations. A regular desktop computer suffices to complete the computations
within minutes as the catalog is of limited size.
3.3 Computing Recommendations
Even though our database contains a large number of events, each type of event
should optimally trigger its own set of recommended movies. On the other hand,
some topics and events are very well connected in the movie database. In some cases,
this leads to a number of recommended movies too large for a recommendation set.
In other cases, too few movies are available to fill a type-specific set (i.e. recommen-
dations based on birthdays). In those cases, our system mixes sets together based on
the types’ similarity (e.g. birthdays and days of death) to achieve a suitable size for a
single set. If we have too many candidates for a set (e.g. 20 birthdays on the same day),
we lower the number of allowed movies per event or select the set of trigger events
randomly for each user.
3.4 Presentation of results
In our VoD scenario, we present the recommendations computed based on the recog-
nized topics on the front page of the VoD portal and in daily newsletters to registered
users.
The topical recommendation of the front page: The landing page of the VoD service
presents the sets of recommended movies. A header shows the type of event used for
this set and creates the topical connection between each movie. To initially spike the
users interest in a recommended movie, the trigger event is presented together with
a short description of the event and, if available, context information on the related
topic.
Theme-focused Newsletters: The VoD service already sends out a daily newsletter to
registered users. This newsletter contains a set of 5 movies that have a deep topical
connection. This connection is represented by a motto, for example, “Directors In-
spired by Quentin Tarantino” or “Dream of a Better Life”. In order to build newsletter
automatically, we compute the most relevant event of the day and compute related
movies. In the next step we try to fill templates created for the newsletters, such as
“Today is the birthday of . His best movies here on ”.
Discussion The implemented system is based on components and can be easily ex-
tended by integrating additional sources or by integrating components tailored to new
types of events. The service interface allows the integration in existing recommender
system.
4 Evaluation
We analyze the recommendations computed by our system. First, we evaluate the
recommendations depending on the type of event used to trigger them. Secondly, we
analyze the relevance of the recommendations and the acceptance of the suggested
movies. The relevance of recommendations is analyzed based on the web server log
of the VoD platform (currently only using content-based item-to-item recommenda-
tions) as well as feedback from experts (curators working for the VoD portal).
Recommendations based on News The system looks for trending topics by analyzing
search log of the VoD system. Tab. 1 shows an overview of the number of events the
recommender extracted based on trending topics for 9 days in January 2015. Analyzing
detected events reveals that several trends in news feeds are correlated with “static”
events. After the death of a popular actor, the time-series of mentions of that actor
oftentimes spikes to an all-time high indicating that people are generally interested
in this type of event. For other events, for example festivals and awards ceremonies,
the number of mentions of the event increases during certain points in time. Awards
ceremonies are often mentioned shortly after nominees for prices are announced,
during the award ceremony itself, and for a short period of time after the event, then
oftentimes together winners of awards. Overall 20% of detected trends can be linked
to “static” events. However, due to the large number of stored events, only 5% of
events are covered by trends. Presenting trends together with a recommended movie
proves to be difficult, if no knowledge is available on what event triggered the trend.
Test users of our web application reacted positively to the most-popular approach,
e.g. presenting the tweet with the highest “favorite” count.
Recommendations based on Static Events Holidays and memorial days days such as
Veterans Day or Mother’s Day are linked to movies based on the similarity between
the description of the holiday and the list of assigned term (retrieved from DB PEDIA)
and the movie description. In our scenario, we considered 706 holidays related to at
least one of the movies in our catalog. Analyzing the impact of these days on the user
behavior, we observed a high variance for country-specific holidays: Displaying the
trigger event together with recommended movie caused users to click 8 times more
often. Confessional holidays increased clicks only by a factor of 1.7, while international
holidays and memorial days increased clicks by a factor of 2.4.
Our birthday database contains 2,570 entries listing on average 7.02 birthdays per
day. These events are related to 3,291 distinct movies. Tab. 1 shows the statistic of
relevant birthdays for the first days in January 2015. In order to evaluate the relevance
of computed recommendations we check the recommendations against the spikes in
the web server log. We found that 23% of recommendations derived from birthdays
could be recognized by an increased movie related activity in the log file.
Table 1. The statistic of events recognized in news and semantic data.
number number example person number of number number example number of
of of related to films related of of person films related
relevant related one of the to the example relevant related whose birthday to this
date trends films trending topics person birthdays films this is person
Jan 1, 2015 0 0 - 0 9 9 Snitz Edwards 1
Jan 2, 2015 2 2 Christian Bale 1 4 6 Lloyd Whitlock 2
Jan 3, 2015 3 4 Van Johnson 2 8 8 Thomas Morris 1
Jan 4, 2015 1 1 Forest Whitaker 1 3 6 August Diehl 4
Jan 5, 2015 6 8 Jessica Chastain 2 7 7 Shea Whigham 1
Jan 6, 2015 5 11 Ethan Hawke 4 13 14 Eddie Redmayne 2
Jan 7, 2015 4 4 Charlie Parker 1 5 6 Nicolas Cage 2
Jan 8, 2015 5 5 Marcus Vetter 1 6 7 Sarah Polley 2
Jan 9, 2015 8 13 Jodie Whittaker 3 11 43 Harun Farocki 32
Death-Days: Compare to birthdays, our database provides a significant smaller num-
ber of dates of death. The dataset contains 581 entries covering 286 days of the year.
These events are related to 729 distinct movies. Similar to the birthday recommender,
most death-days are not recognizable as peaks in the web server log. The death of a
person (detected in the news stream) results in an increased user interest. The dates
of death retrieved from DB PEDIA relate to dates several years in the past. This explains
the different impact of death dates retrieved from the semantic database from death
dates detected in the news.
Discussion We showed that our approach allows us to provide useful recommenda-
tions without having access to user profiles. The impact of the recommendations
depends on the type of the identified event. In general, the relevance of dynamically
recognized events (death of an actor, an award won by an actor) is more relevant
than “static” events retrieved from a knowledge data base. “Big” birthdays of pop-
ular persons are more relevant than “usual” birthdays. Nevertheless “static” events
are valuable since these events ensure that we reliably provide a fixed number of
recommendations and diversify the result set.
Recommending movies based on events is often unexpected to users. In our
discussions with users and the experts from the VoD portal we got positive feedback
for the approach. In order to accept the recommendations it is important that users
know or at least are interested in the events because the relevance of the events is
crucial for the acceptance of the movie recommendations. On the other hand, our
approach helps users to discover new content by recommending items based on
events users usually would not be aware of.
5 Conclusions and Future Work
In this paper we present our system providing topical film recommendations based
on different types of events. We discussed how to detect potentially relevant events
from news and social media streams as well as the integration of semantic knowledge
sources. In our analysis we found that birthdays of artists have only a very small
influence on the user behavior. Events detected in news streams are better suited for
recommending movies.
In contrast to traditional CF-based approaches, the developed approach helps
users to discover new films. The relevance of suggestions is based on the similarity
with current events instead of the similarity with entries in the user profile. We cur-
rently work on two personalization approaches. We combine the relevance scores
computed using collaborative filtering ensuring that the identified events are match-
ing the individual user preferences. In addition, we plan to allow users to add own
sources ( feeds). This ensures that the news streams providing the basis for recog-
nizing the relevant event meet the user needs.
Furthermore, we work on combining Named Entity Recognition algorithms and
similarity computations based on semantic graphs for detecting movies related to cur-
rent news. The weighted aggregation of several different relevance measures ensures a
higher significance of recommended movies and provides the basis for more detailed
explanations. The presented concept for recommending movies can be easily adapted
for many additional scenarios, such as online shops. The use of the recent news (or
weather data) is a promising new paradigm providing relevant recommendations
without requiring detailed (sensitive) user profiles. A careful selection of sources ana-
lyzed for detecting events ensures that the recommendations are relevant for specific
target groups. Based on the feedback we received for the implemented prototype
there is a high potential in this approach.
Acknowledgments The work has been partially done in the EEGoF project supported by
the German Federal Ministry for Economic Affairs and Energy. The research leading to these
results was performed in the CrowdRec project, which has received funding from the EU 7th
Framework Programme FP7/2007-2013 under grant agreement No. 610594.
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