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Politecnico di Milano Top-N recommendations on Unpopular Items with Contextual Knowledge Paolo Cremonesi Antonio Tripodi Roberto Turrin Politecnico di.

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Presentation on theme: "Politecnico di Milano Top-N recommendations on Unpopular Items with Contextual Knowledge Paolo Cremonesi Antonio Tripodi Roberto Turrin Politecnico di."— Presentation transcript:

1 Politecnico di Milano Top-N recommendations on Unpopular Items with Contextual Knowledge Paolo Cremonesi Antonio Tripodi Roberto Turrin Politecnico di Milano ContentWise

2 Today recommendations, based on your personal taste, are: From this…. To this iTV with personalization

3 Personalization: how it works USER DATA USERS TASTE FRUTIONS AND RATINGS CONTENT METADATA RECOMMENDER SYSTEM CONTENT RECOMMENDATIONS

4 4 CustomersService ProviderNetwork Provider Content Provider IPTV architecture Head end VOD Set-top-box (decoder)

5 user u item i 5

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10 Single domain

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16 Politecnico di Milano Two ideas – One aII from Ricci – Closure (on UU and II)

17 Politecnico di Milano Inserire disegnino IPTV Recommender systems can be divided into two families – Content-Based Filtering – Collaborative Filtering CB algorithms are preferred – Do not rely on metadata (very difficult to obtain in the TV domain) – Quality has been proved to be better in terms of accuracy an serendipity, if the system has been trained with enough data

18 Politecnico di Milano CF algorithms can be classified into – Model-based (able to deal with new users) – Non nodel-based (not able to deal with new users)

19 Politecnico di Milano Top-N recommendations on Unpopular Items with Contextual Knowledge Paolo Cremonesi Paolo Garza Elisa Quintarelli Roberto Turrin Politecnico di Milano ContentWise Short version

20 Politecnico di Milano Thanks for your attention Q&A For any further information, please contact Paolo Cremonesi

21 Politecnico di Milano Top-N recommendations on Unpopular Items with Contextual Knowledge Paolo Cremonesi Paolo Garza Elisa Quintarelli Roberto Turrin Politecnico di Milano ContentWise Long version

22 Politecnico di Milano Research objectives Focus – Top-N recommendation task Goal – Improving accuracy – Providing explanation Requirements – Modularity (algorithm-independent) – Fast on-line recommendations

23 Politecnico di Milano Algorithms 23 Accuracy CorNgbr Non-personalized NeighborhoodLatent factors NNCosNgbr AsySVDPureSVD Collaborative TopPop MovieAvg

24 Politecnico di Milano TopPop and MovieAvg 24 Accuracy CorNgbr Non-personalized NeighborhoodLatent factors NNCosNgbr AsySVDPureSVD Collaborative TopPop MovieAvg Recommends the top-N popular items (i.e., the most rated items), regardless the user preferences and taste

25 Politecnico di Milano TopPop Pirates of the Caribbean: The Curse of the Black Pearl Forrest Gump The Lord of the Rings: The Two Towers The Lord of the Rings: The Fellowship of the Ring The Sixth Sense

26 Politecnico di Milano Collaborative - Neighborhood 26 Accuracy CorNgbr Non-personalized NeighborhoodLatent factors NNCosNgbr AsySVDPureSVD Collaborative TopPop MovieAvg They recommend items according to the approach: who bought this also bought this.. Amazon like …

27 Politecnico di Milano Collaborative – Latent factors 27 Accuracy CorNgbr Non-personalized NeighborhoodLatent factors NNCosNgbr AsySVDPureSVD Collaborative TopPop MovieAvg They recommend items on the basis of an advanced representation of users and items in a low-dimensional feature space

28 Politecnico di Milano Contextual recommendations Pre-filtering – L.Baltrunas, F.Ricci RecSys'09 Post-filtering – U.Panniello, A.Tuzhilin, M.Gorgoglione,... RecSys'09 Contextual modeling – M.Domingue, A.Jorge, C.Soares RecSys'09 – C. Palmisano, A.Tuzhilin, M.Gorgoglione IEEE Trans. Knowl. Data Eng., 2008

29 Politecnico di Milano Association rules Data mining technique Uses frequency based approach to find conditional probability of events Forrest Gump and Nikita Avatar

30 Politecnico di Milano Association rules XY X = previously watched movie(s) Y = movie(s) the user will likely appreciate Quality of association rules: – Support: frequency of the rule – Confidence: conditional probability of Y given X Benefits (by definition) – best recommendations in terms of accuracy

31 Politecnico di Milano Association rules and RS Sarwar et al. Analysis of recommendation algorithms for e-commerce, EC 2000 Computational requirements – theoretically we should test for all the possible combinations of items in X and Y Portfolio effect – most rules find the same small set of consequents – recommendations are biased toward obvious items

32 Politecnico di Milano Portfolio effect

33 Politecnico di Milano Portfolio effect Which is the most simple and yet most effective association-rule based recommender system?

34 Politecnico di Milano Portfolio effect Which is the most simple and yet most effective association-rule based recommender system? TopPop

35 Politecnico di Milano Recall on Netflix AlgorithmRecall at 10 PureSVD NNCosNgbr0.45 AsySVD0.30 TopPop0.28 CorNgbr0.15 MovieAvg0.12

36 Politecnico di Milano Recall on Netflix AlgorithmRecall at 10 long tail only PureSVD NNCosNgbr AsySVD0.30 TopPop CorNgbr MovieAvg0.12 Removed the most popular items accounting for 33% of ratings

37 Politecnico di Milano Measured perceived quality Users judgments on Accuracy and Novelty Participants: 30 users per 7 experimental condition 210 users overall Profile: years old male: 54% - female: 46%

38 Perceived relevance

39 Perceived novelty

40 Politecnico di Milano Context recommender system Traditional Recommender System Users contexts - Items characteristics Contextual Rule Mining RecommendationsContextual rules Contextual Post-filtering Contextual Recommendations Users - Items

41 Politecnico di Milano Experiments: rules mining Movielens: 1 M ratings – 1000 users – 1700 items Context – # age ranges = 7 – # gender = M/F Movie features – # genres = 18 Rules mined with FP-growth Min support = 1000

42 Politecnico di Milano Goal Identify correlations between users context and item characteristics Filter predictions performed by a traditional recommender

43 Politecnico di Milano Inputs to the system Input to the recommender system URM Input to the contextual rule miner CFM – User context × Item features – number of ratings users in context c gave to items with feature f

44 Politecnico di Milano Creation of the transactional dataset UCM Transactional dataset Example: A rating given by a Male with age [20-25] to a fantasy movie (gender = M) (age = [20-25]) (genre = fantasy) is included in the transactional dataset

45 Politecnico di Milano Rule mining: example The following two rules are extracted for the context (gender = M): (gender = M) ) (genre = horror) (gender = M) ) (genre = action) It follows that only horror and action movies can be recommended to male users

46 Politecnico di Milano Example of rules … GenderAgeGenreProb.Support F35-44Drama35%17000 Comedy32%16000 Romance18%9000 Childrens8%4000 Musical5%2500 Animation4%2000 Mystery4%2000 Fantasy3%1500

47 Politecnico di Milano Example of rules … GenderAgeGenreProb.Support M35-44Action23%34000 Thriller16%24000 Sci-Fi14%21000 Adventure12%17000 War7%10000 Horror6%9000 Mystery3%5000 Western2%3000 Noir2%3000

48 Politecnico di Milano Two options Keep all of the rules Keep only rules with a large confidence 15% In any case, we keep only rules with a large support (>1000 ratings)

49 Politecnico di Milano Example of rules … GenderAgeGenreProb.Support F35-44Drama35%17000 Comedy32%16000 Romance18%9000 Childrens8%4000 Musical5%2500 Animation4%2000 Mystery4%2000 Fantasy3%1500

50 Politecnico di Milano Example of rules … GenderAgeGenreProb.Support M35-44Action23%34000 Thriller16%24000 Sci-Fi14%21000 Adventure12%17000 War7%10000 Horror6%9000 Mystery3%5000 Western2%3000 Noir2%3000

51 Politecnico di Milano Results

52 Politecnico di Milano Recall at 5 on long-tail items Long tailWithout contextWith context PureSVD21%30% AsySVD7%12% NNCosNgbr10%24% CorNgbr4%9% TopPop0.1%8% MovieAvg1%5% Removed 5% of the most popular items Accounting for 33% of ratings

53 Politecnico di Milano Recall at 5 on all items Whole datasetWithout contextWith context PureSVD39%38% (22%) AsySVD18%19% (11%) NNCosNgbr35%35% (21%) CorNgbr10%11% (9%) TopPop20%21% (13%) MovieAvg2%5%

54 Politecnico di Milano Recall with non-personal methods

55 Politecnico di Milano Recall with neighborhood methods

56 Politecnico di Milano Recall with latent-factors methods

57 Politecnico di Milano Thanks for your attention Q&A For any further information, please contact Paolo Cremonesi


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