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IPTV Recommender Systems
 

IPTV Recommender Systems

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    IPTV Recommender Systems IPTV Recommender Systems Presentation Transcript

    • Paolo Cremonesi IPTV Recommender Systems
    • 2 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
    • 3 Valentino Rossi Paolo Cremonesi - Recommender Systems
    • 4 IPTV architecture Live TV Set-top-box (decoder) VOD Content Provider Service Provider Network Provider Customers Paolo Cremonesi - Recommender Systems
    • 5 IPTV architecture • IPTV is a video service supplied by a telecom service provider that owns the network infrastructure and controls content distribution over the broadband network for reliable delivery to the consumer (generally to the TV/IP STB). • Services Broadcast TV (BTV) services which consist in the simultaneous reception by the users of a traditional TV channel, Free-to-air or Pay TV. BTV services are usually implemented using IP multicast protocols. Video On Demand (VOD) services, which consist in viewing multimedia contents made available by the Service Provider, upon request. VOD services are usually implemented using IP unicast protocols. Paolo Cremonesi - Recommender Systems
    • 6 IPTV Platform: Now CUSTOMERS FACE DIFFICULTIES FINDING HUNDREDS THE “RIGHT” CONTENT LIVE CHANNELS THOUSANDS CUSTOMER VOD PURCHASES ITEMS CUSTOMER FRUSTRATION Paolo Cremonesi - Recommender Systems
    • 7 IPTV Platform: with a recommender systems From this…. Today recommendations, based on your personal taste, are: To this. Paolo Cremonesi - Recommender Systems
    • 8 IPTV recommender needs • Improve user satisfaction • Sell new content to users VOD Pay-per-view channels • Targeting advertisement Paolo Cremonesi - Recommender Systems
    • 9 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
    • 10 Recommender System: how it works USER’S TASTE USER CONTENT DATA FRUTIONS AND METADATA RATINGS RECOMMENDER SYSTEM CONTENT RECOMMENDATIONS Paolo Cremonesi - Recommender Systems
    • 11 Problem formulation Users ratings Items metadata Recommender Ranked list •Item1 •Item2 Top N •Item3 •. •. •. •ItemX Paolo Cremonesi - Recommender Systems
    • 12 Recommendation techniques Recommender algorithms Collaborative Filtering User Item Content-based based based Filtering Users with similar taste Similar Items Paolo Cremonesi - Recommender Systems
    • 13 Memory vs. model based Memory Model based based User-based X Item-based X Dimensional- X reduction Content-based X Paolo Cremonesi - Recommender Systems
    • 14 Collaborative Filtering User-based similar users rate an item 4 5 similarly ? 3 Item-based similar items are rated by a user similarly 2 2 Neighborhood NB: similarity means correlation Paolo Cremonesi - Recommender Systems
    • 15 Collaborative filtering: User Rating Matrix User Item Paolo Cremonesi - Recommender Systems
    • 16 User rating matrix URM I1 I2 I3 I4 I1 I2 I3 I4 U1 3 4 0 1 U1 0 1 0 1 U2 2 2 1 0 U2 0 0 1 1 U3 2 0 0 4 U3 1 1 0 0 U4 1 5 0 1 U4 1 1 0 1 U5 3 0 1 0 U5 0 1 1 1 Explicit URM Implicit URM Paolo Cremonesi - Recommender Systems
    • 17 Dimensional-reduction collaborative model • items and users can be described by a number (K) of unknown features • auf : describes if feature f is important for user u • bif : describes if feature f is present in item i • rui : rating assigned by (or estimated) user u to item i k rui = f =1 auf · bif Paolo Cremonesi - Recommender Systems
    • 18 Singular Value Decomposition mxn R A = U S VTT V mxn mxk kxk kxn Sk VkT Rk A = Uk Paolo Cremonesi - Recommender Systems
    • 19 Singular Value Decomposition T R=U·S·V T V·V =I T U ·U=I Paolo Cremonesi - Recommender Systems
    • 20 Singular Value Decomposition T R k = U k · Sk · Vk T Vk · Vk =I T Uk · Uk = I Rk : best rank-k approximation of R according to the Frobenious norm not according least square error!! Paolo Cremonesi - Recommender Systems
    • 21 Folding-in • New rows/columns of A are projected (folded-in) in the existing latent space without computing a new SVD • e.g., a new user u u’ = u Vk Sk-1 Sk Vk Ak Uk u u’ Paolo Cremonesi - Recommender Systems
    • 22 Collaborative Filtering: pro & cons • Pro: There is no need for content • Cons: Cold Start: we needs to have enough users in the system to find a match. Sparsity: when the user/ratings matrix is sparse it is hard to find a neighbourhood. First Rater: cannot recommend an item that has not been previously rated anyone else Popularity Bias: cannot recommend items to someone with unique tastes. Tends to recommend popular items (dataset coverage) Paolo Cremonesi - Recommender Systems
    • 23 Content-based Filtering Term 3 ...mettendo a punto una scoperta che potrebbe portare al primo uso terapeutico della controversa procedura. Se gli studi animali si riveleranno promettenti, i ricercatori potrebbero cominciare a mettere alla prova le nuove cellule su occhi umani da qui a due anni... 2 Term 1 m Ter • Similar items contain the same terms • The more a term occurs in an item, the more representative it is • The more a term occurs in the collection, the less representative it is (i.e. it is less important in order to distinguish a specific item) Paolo Cremonesi - Recommender Systems
    • 24 Content-based filtering: Item-Content Matrix Word Item Paolo Cremonesi - Recommender Systems
    • 25 Content-based Filtering: techniques Term 3 User-item similarity Term 1 2 m Ter Paolo Cremonesi - Recommender Systems
    • 26 Content-based Filtering: pro & cons • Pro: No need for data on other users No cold-start or sparsity problems, neither first-rater • Able to recommend to users with unique tastes • Able to recommend new and unpopular items Can provide explanations about recommended items Well-known technology • Cons: Requires a structured content Lower accuracy Users tastes must be represented as a function of the content Unable to exploit quality judgments of other users Paolo Cremonesi - Recommender Systems
    • 27 Content-based Filtering: Latent Semantic Analysis svd Sk Vk T A Ak = Uk mxn -Terms in rows Vk * sqrt (Sk) -Items in columns Uk * sqrt (Sk) pseudo terms pseudo items cosine Ak Paolo Cremonesi - Recommender Systems
    • 28 Recommender architecture Resources management Features Features Items Storage extraction representation Filter Compute user- Items Items item correlation retrieval recommendation Users management Infer and learn Interests/tastes Users profile representation feedback Explicit vs implicit ratings Paolo Cremonesi - Recommender Systems
    • 29 Datasets Real datasets composed by movies and user fruitions, plus some extra information • User-item rating matrix 23942 users 564 movies 56686 ratings • Movie Meta-data (textual information) Title Genre Director Cast Duration … Paolo Cremonesi - Recommender Systems
    • 30 • Implicit vs Explicit • Come determinare il rating implicito VOD TV (EPG) Paolo Cremonesi - Recommender Systems
    • 31 Some problems with IPTV recommender • Cold start • Multi-language content (e.g., Switzerland) • New user problem (user-based algorithms) • New item problem (all collaborative algorithms) • Semantic problem (e.g., house and home) Paolo Cremonesi - Recommender Systems
    • 32 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
    • 33 Problem • Many works do not describe clearly the methods used for performance evaluation and model comparison • Different dataset partition methodology and evaluation metrics lead to divergent results • The Netflix prize has improperly focused the research attention on Hold-out RMSE Paolo Cremonesi - Recommender Systems
    • 34 Objective • Design a new methodology to compare different algorithms according to 34 how often the user watches the TV (length of user profile) if the user prefers “blockbuster” movies (user preference versus popular or unpopular movies and programs) • Design a multi-model system Paolo Cremonesi - Recommender Systems
    • 35 Metrics • Error metrics • Accuracy metrics Mean Square Error Recall (MSE) Precision Root Mean Square Error Fallout (RMSE) F-measure Mean Absolute Error (MAE) ☺ Both implicit and explicit datasets Only for explicit datasets Top-N recommender systems Paolo Cremonesi - Recommender Systems
    • 36 Accuracy metrics Paolo Cremonesi - Recommender Systems
    • 37 Accuracy metrics Paolo Cremonesi - Recommender Systems
    • 38 Accuracy metrics Paolo Cremonesi - Recommender Systems
    • 39 Netflix dataset: test user profile Paolo Cremonesi - Recommender Systems
    • 40 Netflix dataset: Global effects algorithm RMSE: 0.95 Recall: 1% F-measure: 0.01 Paolo Cremonesi - Recommender Systems
    • 41 Netflix dataset: Adjusted cosine algorithm RMSE: 1.6 Recall: 8% F-measure: 0.16 Paolo Cremonesi - Recommender Systems
    • 42 Netflix dataset: SVD algorithm RMSE: 2.7 Recall: 17% F-measure: 0.28 Paolo Cremonesi - Recommender Systems
    • 43 Quality evaluation • Focus on future performance on new data • Proper partitioning of original data set into: 43 training set test set • Test set must be different and independent from training set • Active user: should be left out of the model Paolo Cremonesi - Recommender Systems
    • 44 Hold-out Paolo Cremonesi - Recommender Systems
    • 45 Leave-one-out Paolo Cremonesi - Recommender Systems
    • 46 K-fold Paolo Cremonesi - Recommender Systems
    • 47 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
    • 48 Recommender system architecture Inputs Real time calls Business Rules Web Items’ Content Services STB client (ICM) Batch Real-time STB … Processing Recommendation server STB client Users’ Ratings (URM) Model Repository Paolo Cremonesi - Recommender Systems
    • Proposed approach • Batch system Statistical analysis of the dataset Definition of a number of models Accuracy evaluation for different user profiles • Run-time system User profile analysis Selection of best candidate model Recommendation Paolo Cremonesi - Recommender Systems
    • Multi-model recommender engine Paolo Cremonesi - Recommender Systems
    • 51 Dataset statistical analysis (example) Paolo Cremonesi - Recommender Systems
    • 52 Dataset statistical analysis (example) 1 Percentage of rated items in the top-rated NM ML 0.75 NF 0.5 0.25 0 0 1 2 3 4 10 10 10 10 10 Position of the items in the top-rated Paolo Cremonesi - Recommender Systems
    • 53 Dataset statistical analysis (example) User groups Item popularity 20 or more Popular 10...19 2...9 Non-Popular Paolo Cremonesi - Recommender Systems
    • 54 Popular vs. unpopular: SVD algorithm - NF 0.35 all 0.3 popular unpopular 0.25 0.2 Recall 0.15 0.1 0.05 0 0 200 400 600 800 1000 Latent size Paolo Cremonesi - Recommender Systems
    • 55 Popular vs. unpopular: SVD algorithm - NM all popular 0.25 unpopular 0.2 Recall 0.15 0.1 0.05 5 15 50 100 200 300 Latent size Paolo Cremonesi - Recommender Systems
    • 56 User profile length – NM recall Group SVD Cos -like NBN_S NBN_I NBN_U 2 -9 11,21% 19,35% 19,65% 17,23% 21,65% All 10 -19 11,23% 13,11% 12,09% 13,62% 12,60% 20 -inf 9,91% 8,01% 6,45% 6,65% 6,52% Group SVD Cos -like NBN_S NBN_I NBN_U 2 -9 22,21% 31,21% 31,54% 26,17% 33,93% Popular 10 -19 24,12% 27,12% 24,61% 27,36% 25,59% 20 -inf 25,72% 22,71% 20,71% 21,14% 20,93% Group SVD Cos -like NBN_S NBN_I NBN_U Unpopular 2 -9 9,92% 0,81% 0,13% 2,64% 1,48% 10 -19 10,01% 1,23% 0,19% 0,56% 0,25% 20 -inf 10,14% 0,70% 0,01% 0,10% 0,01% Best average algorithm (item-based) 15,94% Multi-model (overall) 20,92% Paolo Cremonesi - Recommender Systems