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Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
Extending Recommendation Systems With Semantics And Context Awareness
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Extending Recommendation Systems With Semantics And Context Awareness

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  • 1. Extending recommendation systems with semantics and context-awareness CCIA 2011 Victor Codina & Luigi Ceccaroni vcodina@lsi.upc.edu lceccaroni@BDigital.orgDepartament de Llenguatges i Sistemes Informàtics Health InformaticsKnowledge Engineering and Machine Learning Group Personalized Computational Medicine
  • 2. Outline Traditional vs. Contextual recommendation State-of-the-art & Current limitations Research question Semantics acquisition & exploitation Proposed model Experimental evaluation Conclusions & Future work Extending Recommendation Systems with Semantics and Context-Awareness 2
  • 3. Traditional recommendation problem Regression problem: o Given a pair (u ∈ U, i ∈ I), predict item’s degree of utility ( ) Estimation based only on user and item information preferences (u) Preference Collaborative filtering (CF) Content-based (CB) Hybrid Recommendation model attributes (i) Matrix recommender Extending Recommendation Systems with Semantics and Context-Awareness 3
  • 4. Context-aware recommendation problem Context as additional dimension for estimation o Given a tuple (u, i, c), predict item’s degree of utility in context c o Context = “situated action” Training dataRepresentational view: c Pre-filtering c Recommendation model Multi-Dimensional (MD)Example: c = (winter, cold) c Post-filtering c1 = Season c2 = Temperature Extending Recommendation Systems with Semantics and Context-Awareness 4
  • 5. State-of-the-art & limitations Adaptations of latent-factor models (MD paradigm) Examples: o N-dimensional Tensor Factorization o Bias-based Matrix Factorization with temporal dynamics Best prediction accuracy results on recent competitions o E.g.: Netflix challenge (2009), Yahoo! Labs KDD Cup (2011) Main limitations of latent-factor models: o Lack of transparency in explaining recommendations o Low cold-start performance (users and items with few ratings) o Lack of novelty and diversity of recommendations Extending Recommendation Systems with Semantics and Context-Awareness 5
  • 6. Research questions & main assumptions Research questions o Q1. Can we overcome the limitations and improve global recommendation quality (not only prediction accuracy) by exploiting domain and context knowledge? o Q2. Under which conditions is this improvement maximized? Main assumptions o There exists semantic relationships among entities of the recommendation space (users, items, contexts) o The adequate exploitation of these semantic relationships is useful to overcome current limitations Extending Recommendation Systems with Semantics and Context-Awareness 6
  • 7. Knowledge acquisition and representationDomain/Context Concept Concept concepts x y S(x,y)? Explicit similarity Implicit similarity Ontology-based Statistics-basedSimilarity measure - Edge-based (LCA) - Probabilistic measures (PMI) - Node-based (MICA) - Dimensionality reduction (LSA) - Logic-based - Graph-based (SimRank) uses uses Ontologies Data collectionsKnowledge source - Folksonomies - Taxonomies (ODP) - Thesauri (Wordnet) - Item descriptions Extending Recommendation Systems with Semantics and Context-Awareness 7
  • 8. User/Item representation Concept-based modeling (weighted overlay approach) Domain knowledge (concepts = item attributes) User ‘u’ d2 d4 Item ‘i’ Pu Pi d1 d3 (Degree of interest in d1) (Relevance of d3) Interest inferring method Attribute weighting method - Explicit feedback (Rating avg) - Structured content (IDF) - Implicit feedback (Seen frequency) - Unstructured (TFIDF, tagshare) Extending Recommendation Systems with Semantics and Context-Awareness 8
  • 9. Knowledge exploitationKnowledge Can be used for… Possible benefits type Measuring the semantic matching among Less rigid contextualContextual different context states filtering than using exact matching Applying semantic inference methods over Enrich item/user user/item concept-based profiles: profiles with new - Spreading activation concepts - Reasoning based on DLs semantically related Domain- based Measuring the matching between two More precise user/item using various semantic matching similarity strategies: measurements that - Pairwise (Best-pairs or All-pairs) using traditional - Groupwise (set-, vector- or graph-based) measures Extending Recommendation Systems with Semantics and Context-Awareness 9
  • 10. Case of study: a MD semantically-enhanced CB Contextual prediction model (bias-based): Overall Contextual Contextual rating avg User bias Item bias All-pairs Item-User semantic matching where: Session bias of (u,d) contextual bias of (u,d) Stochastic gradient descent for model training: Extending Recommendation Systems with Semantics and Context-Awareness 10
  • 11. MovieLens Dataset Contextual concepts without semantics o 3 contextual factors (season, time of the day, weekend?) Domain concepts with implicit semantics o Set of pre-selected tags + set of genres o Semantic relationships among tags acquired from folksonomy Original dataset pruned by selecting only items with a certain amount of pre-selected tags Extending Recommendation Systems with Semantics and Context-Awareness 11
  • 12. Offline experiment Last ratings (according to timestamp) testing o In this way we simulate future predictions for each user 5-fold cross validation Two recommendation tasks evaluated o Rating prediction (RMSE) and Top-10 recommendation (Recall) Threshold-based cold-start performance evaluation o User profile size < 25 ratings: 10% of users Performance comparison of the proposed model with: o 3 model variants o 5 baseline models o 1 model based on matrix factorization Extending Recommendation Systems with Semantics and Context-Awareness 12
  • 13. Results Paired t test significance among 4 model variants: o Model 1 “Static-CB” (static bias + traditional Item-User matching) o Model 2 “Static-SemCB” (static bias + “All-pairs” matching) o Model 3 “Contextual-CB” (contextual bias + traditional matching) o Model 4 “Contextual-SemCB” (contextual bias + “All-pairs” matching) Global RMSE Cold-Start RMSE0,851 0,9190,844 0,9180,837 0,917 0,05 0,001 0,17 0,05 0,62 0,01 0,83 0,916 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 (P-values in red) E.g. P-value = 0,05 means that there is a 95% chance of being a real difference Extending Recommendation Systems with Semantics and Context-Awareness 13
  • 14. Conclusions Context-awareness improves prediction accuracy for users with a certain number of ratings (non cold-start) o 25+ rating: 90% of users Semantics slightly improves cold-start performance The knowledge acquisition method for the MovieLens folksonomy may be not adequate: limited domain knowledge MovieLens users rate several movies at once and not just after seeing the movie o Rating-session--specific effects have a major influence in the user ratings: distorted contextual information Extending Recommendation Systems with Semantics and Context-Awareness 14
  • 15. Future work Extending evaluation of the proposed CB model: o Using datasets from other domains (e.g. music, tourism, health) o Experimenting with other sources of knowledge (e.g. Amazon movie taxonomy) o Experimenting with other methods for semantics exploitation o Evaluating other properties (e.g. diversity, novelty, coverage) Extending CF models with the proposed semantic approach: o Neighborhood-based o Matrix Factorization Extending Recommendation Systems with Semantics and Context-Awareness 15
  • 16. Extending recommendation systems with semantics and context-awareness CCIA 2011 Victor Codina & Luigi Ceccaroni vcodina@lsi.upc.edu lceccaroni@BDigital.orgDepartament de Llenguatges i Sistemes Informàtics Health InformaticsKnowledge Engineering and Machine Learning Group Personalized Computational Medicine
  • 17. Backup slidesExtending Recommendation Systems with Semantics and Context-Awareness 17
  • 18. Prediction models of all variants Model 1 (Static-CB): Model 2 (Static-SemCB): Model 3 (Contextual-CB): Model 4 (Contextual-SemCB): Extending Recommendation Systems with Semantics and Context-Awareness 18

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