De Pessemier, Toon

Martens, Luc

English

Ghent University Academic Bibliography

A profile based recommendation system forTV-Anytime annotated contentToon De PessemierSupervisor(s): Luc MartensAbstract— The enormous offer of video content on the internet and TVbroadcast networks requires new instruments to assist people in selectingthe appropriate content. Currently, video retrieval is done with one dimen-sional keyword based search tools, which are far too inadequate to filter outall the irrelevant content. User ratings could improve the search results butthis method has a big dependency on the community itself.This paper introduces a TV-Anytime based personalisation system thatrecommends video content according to the individual user profile. By con-tinuously monitoring user interactions, this profile gets updated and reflectsthe user’s interests. These monitored user interactions are twofold: implicitfeedback of the viewing behaviour and explicit user feedback consisting ofratings. A complete test setup has been deployed to evaluate the user in-teraction mechanism and the designed recommendation algorithm. Theresults of the user tests are presented in this study.Keywords— Personalisation, Algorithm, User Profiling, Recommenda-tion, TV-AnytimeI. INTRODUCTIONNOWADAYS , the offer of video content on the internet isabundant and is still increasing. The web 2.0 video siteslike YouTube and Metacafe, are extremely popular. In addition,the number of available TV channels has increased enormouslyby the introduction of digital television.These new services complicated video retrieval for the enduser who can only consume a small fractions of the overwhelm-ing quantity of video content. Furthermore, a lot of videos areannoying, irrelevant or not in the field of interest of the user.Most video sites use a keyword based search tool to addressthese issues. Nevertheless, this rudimentary search tool is notcapable to weed out irrelevant content. A second filtering basedon user ratings can assist, but has a big dependency on the com-munity itself.The situation for digital TV is even worse. To find the mostsuited television programme, one has to thumb through a thickprinted TV guide, or make an appeal to an Electronic ProgramGuide (EPG). However, most of these EPGs still lack intelli-gence and do not provide a personalised solution [1].To solve this problem, we built a recommendation system,that dynamically creates a profile for each viewer and recom-mends the video content that best matches this profile. TheTV-Anytime metadata standard [2] is used for the descriptionof these content items in our implementation. This recommen-dation system can be deployed in an internet video service, me-diagateway or EPG to assist the consumers in choosing the ap-propriate content [3].T. De Pessemier is a student of the Ph. D. fellowship of the Research Foun-dation - Flanders (FWO) and is a member of the Wireless and Cable group ofthe Department of Information Technology, Ghent University (UGent), Gent,Belgium. E-mail: Toon.Depessemier@intec.UGent.be .II. THE PROFILE STRUCTUREThe user profile consists of a set of metadata terms ti,e. g. “soccer”, “Antonio Banderas”, “violence”, etc. Each term,ti, belongs to a field, fi, where∀i : fi ∈ { Genre, Actor, Director, Coworker, Keyword,Spoken language, Title, Caption language }A user appreciation, ui, varying from -1 to +1, is associated witheach term as well. Therefore, the profile is stored in the databasein the form of 3-tuples, (ti, fi, ui).All metadata information is not equally important. For in-stance, a content genre, may be more significant than a keyword.As a result, we should assign an importance factor, W (fi), toreflect the relative importance of each field, fi. The values ofW (fi) used in our implementation are fixed (Fig. 1) and weredetermined by a small user survey before the user tests. In fu-ture versions, the value of these weights could be optimalizedthrough time-dependency and personalisation.These 3-tuples are constantly updated in an implicit and ex-plicit way. The time the user spends on watching the video willbe used to implicitly update the user profile. The early termina-tion of a video, for example, could be interpreted by the systemas negative input for the algorithm. The profile can be explicitlyupdated by a star rating system as well. When a person grants ascore to a video, every term ti with field fi of that content item,is updated or inserted into the user profile as (ti, fi, ui) with thefollowing rules:• If there is already a 3-tuple in the profile with specified termti and field fi, its user appreciation is updated:u′ = (1− α) · u+ α · β (1)where u′ stands for the new appreciation of ti, u represents theold appreciation of the term, α is a parameter between 0 and 1that specifies the learning rate and β symbolizes the score fromthe implicit or explicit rating mechanism and ranges from -1 to+1.• If there is not yet a 3-tuple in the profile with specified term tiand field fi, it is inserted with:u′ = β (2)III. RECOMMENDATION ALGORITHMThe following sequence is used by the algorithm to obtain therecommendation score.1. Extract the usable information from the TV-Anytime meta-data of the content item.2. Check which of these terms, ti, are present in the current userprofile.Fig. 1. algorithm example3. Collect the user appreciations, ui, of these terms ti, fromthe user profile and calculate the recommendation score(S) as aweighted average of these user appreciations:S =∑i ui ·W (fi)∑i W (fi)S ∈ [−1, 1] (3)where i iterates over every term, ti, that was selected in step 2,ui is the user appreciation of the term and W (fi) represents theweight of the field, fi.Fig. 1 illustrates this algorithm for the movie “Jurassic Park”and a fictitious user profile.IV. EVALUATIONTo evaluate the system, 10 users were invited to watch contentduring an average of ten days. We requested to consume 5 to 10video fragments each day and explicitly demanded to rate thevideos through granting a number of stars.To evaluate the user tests, we compared the user score (after amapping to the interval [−1, 1]) with the score calculated by thealgorithm before rating. The algorithm uses all the previous rat-ings and watching behavior to predict the user appreciation foreach video. The first thing we noticed, was the little differencein algorithm performance between the several days. Becausean initial user profile was set up by asking the user’s favouritegenres, a reliable notion of the user’s preferences was alreadyavailable. A detailed profile with more terms is needed to im-prove the recommendations. Therefore more data than availableFig. 2. Evaluation: graph1in the user tests and a longer evaluation period is required toachieve a significant performance improvement in time.A representation of our user tests is shown in Fig. 2. Forevery test user, four bars are printed. The first bar stands forthe number of videos that were rated. The second bar representsthe cases in which the user score had the same sign as our al-gorithm score. Predicting the sign of the user score is usefulwhen services have to make a binary choice, e. g. whether ornot to record or offer a specific video. The third bar reports thecases in which the difference between the algorithm score andthe user score, (i.e. the algorithm error) is between -0.6 and 0.6.These predictions have an error of three or less stars. The lastbar are the predictions that have an algorithm error between -0.4and 0.4, which denotes an error of two or less stars. This evalua-tion illustrates the practicability of the algorithm. Nevertheless,there is room for improvement in future algorithm designs.V. CONCLUSIONThis paper proposed a recommendation algorithm that can bedeployed in an internet video service, mediagateway or EPGto assist consumers in selecting content. The structure of theuser profile, a set of 3-tuples, is described together with the up-date mechanism. User profiles are updated in an explicit wayby means of a rating system as well as in an implicit way bylogging the user’s behaviour. We designed a weighted averagerecommendation algorithm that predicts the user appreciationsfor content items based on the user profile. A user evaluationproved the practicability of the recommendations system. Nev-ertheless, the results indicate a margin for beter recommenda-tions in future algorithm designs.REFERENCES[1] Z.Yu and X.Zhou, “TV3P: an adaptive assistant for personalized TV,” IEEETransactions on Consumer Electronics, vol. 50, no. 1, pp. 393–399, feb2004.[2] “Tv-anytime forum,” [Online]. Available: http://www.tv-anytime.org/.[3] H.Lee, J-G.Kim, S-J.Yang, and J.Hong, “Personalized TV services basedon TV-Anytime for personal digital recorder,” IEEE Transactions on Con-sumer Electronics, vol. 51, no. 3, pp. 885–892, aug 2005.   

A profile based recommendation system for TV-anytime annotated content

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A profile based recommendation system for TV-anytime annotated content

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