Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation
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Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation

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UMAP 2014 Presentation

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Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation Presentation Transcript

  • Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendation Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis (Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group) UMAP 2014 22th Conference on User Modeling, Adaptation and Personalization Aalborg (Denmark) July 8, 2014
  • Content-based Recommender Systems Suggest items similar to those the user liked in the past (I bought Converse should, I’ll continue buying similar sport shoes) Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 2
  • Content-based Recommender Systems Xuser profile items Recommendation are generated by matching the features stored in the user profile with those describing the items to be recommended. Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 3 ♥
  • Content-based Recommender Systems (Some) Limitations Poor Semantic Representation Poor Contextual Modeling Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 4
  • ? Lack of Semantics “I love turkey. It’s my choice for these holidays! Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 5
  • Lack of Contextual Modeling Ashtead? Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 in Aalborg: brewery recommendations 6
  • Lack of Contextual Modeling Many content-based recommendation engines do nothandle contextual information (e.g. user location) 1370km ! far away :-) Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 7
  • contextual eVSM a context-aware content-based recommendation framework based on distributional semantics and entity linking Our contribution Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 8
  • Contextual eVSM Workflow Semantic Content Analyzer Context-aware Profiler Recommender Items User Profiles User Ratings Contextual Data Item Description Context-aware Recommendations Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 9
  • Contextual eVSM 3 main components Semantic ! Content Analyzer! Context-aware ! Profiler! Recommender! Items User Profiles User Ratings Contextual Data Item Description Context-aware Recommendations Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 10
  • ! • Input: items to be recommended (along with their textual description) • Output: semantic representation • Novelty: we exploited • Entity Linking algorithms! • Distributional Semantics Models Contextual eVSM Semantic Content Analyzer Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 11
  • Contextual eVSM • Entity Linking Algorithms! • Input: free text. • items description, in our setting • Output: identification of the most relevant entities mentioned in the text. • We adopted: • tag.me(1) , • DBpedia Spotlight(2) , • Wikipedia Miner(3) Semantic Content Analyzer :: Entity Linking Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 (1) http://tagme.di.unipi.it (2) http://spotlight.dbpedia.org (3) http://wikipedia-miner.cms.waikato.ac.nz 12
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Example Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Textual Description (e.g. Wikipedia abstract) Processed Text 13
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Example Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Very transparent and human readable content representation Tag.me output 14
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Example Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Tag.me output non-trivial NLP tasks (stopwords removal, n-grams identification, named entities recognition and disambiguation) are automatically performed 15 Very transparent and human readable content representation
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Example Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Tag.me output Each entity is a reference to a Wikipedia page http://en.wikipedia.org/wiki/The_Wachowskis not a simple textual feature! 16
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Example Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 We enriched this entity-based representation ! by exploiting the Wikipedia categories’ tree 17
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Representation Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 The final representation of each item is obtained by merging the entities identified in the text with all the Wikipedia categories each entity is linked to. +Entities Wikipedia CategoriesFeatures = 18
  • Contextual eVSM Semantic Content Analyzer :: Entity Linking::Representation Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 The final representation of each item is obtained by merging the entities identified in the text with all the Wikipedia categories each entity is linked to. +Entities Wikipedia CategoriesFeatures = Problem: Even such a rich, transparent and human-readable representation does not handle semantics 19
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 “meaning is its use” L.Wittgenstein (Austrian philosopher) 20
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics (*) Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 by analyzing large corpora of textual data it is possible to infer information about the usage (about the meaning) of the terms Insight similar meaning co-occurrence co-occurrence co-occurrence co-occurrence (*) Firth, J.R.A synopsis of linguistic theory 1930-1955. In Studies in Linguistic Analysis, pp. 1-32, 1957. 21
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics::WordSpace Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 beer wine mojito dog 22 Vector-space representation is based on term co-occurences
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 e1 e2 e3 e4 e5 e6 e7 e8 e9 Keanu Reeves ✔ ✔ ✔ ✔ ✔ Al Pacino ✔ ✔ American Writers ✔ ✔ ✔ ✔ Laurence Fishburne ✔ ✔ ✔ ✔ Our Semantic Content Analyzer learns a vector-space item representation based on distributional semantics models 23
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 e1 e2 e3 e4 e5 e6 e7 e8 e9 Keanu Reeves ✔ ✔ ✔ ✔ ✔ Al Pacino ✔ ✔ American Writers ✔ ✔ ✔ ✔ Laurence Fishburne ✔ ✔ ✔ ✔ Vector-space Semantic Representation is learnt according to entities co-occurrences in textual descriptions 24
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Unexpected connections between entities can be learnt in a total unsupervised way thanks to Distributional Semantics 25
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 e1 e2 e3 e4 e5 e6 e7 e8 e9 Keanu Reeves ✔ ✔ ✔ ✔ ✔ Al Pacino ✔ ✔ American Writers ✔ ✔ ✔ ✔ Laurence Fishburne ✔ ✔ ✔ ✔ e.g. Keanu Reeves and Al Pacino both starred in Drama movies 26
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 How to exploit Distributional Semantics ! to represent items to be recommended? Question 27
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 e1 e2 e3 e4 e5 e6 e7 e8 e9 Keanu Reeves ✔ ✔ ✔ ✔ ✔ Drama ✔ ✔ American Writers ✔ ✔ ✔ ✔ Laurence Fishburne ✔ ✔ ✔ ✔ semantic representation of the items is obtained by combining the vector-space representation of the features which describe them. 28
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 e1 e2 e3 e4 e5 e6 e7 e8 e9 Keanu Reeves ✔ ✔ ✔ ✔ ✔ Al Pacino ✔ ✔ American Writers ✔ ✔ ✔ ✔ Laurence Fishburne ✔ ✔ ✔ ✔ Matrix ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ 29
  • Contextual eVSM Semantic Content Analyzer :: Distributional Semantics Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Matrix Matrix Revolutions Donnie Darko Up! It is possible to perform similarity calculations between items according to their semantic representation 30
  • ! • Input: • user preferences (ratings) • contextual information • Fixed set of contextual dimensions (company, mood, task, etc.) • Fixed set of values (e.g. company=alone, friends, girlfriend, etc.) • Output: contextual user profiles • Novelty: we introduced a Context-aware Profiling Strategy based on Distributional Models Contextual eVSM Context-aware Profiler Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 31
  • C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy Let’s go straight to the formula 32
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy Let u be the target user Let ck be a contextual variable (e.g. task, mood, etc.) Let vj be its value (e.g. task=running, mood=sad, etc.) 33 C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj)
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) A context-aware profile can be learnt by combining two components in a linear fashion 34
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) a non-contextual representation of user preferences a vector space representation of the context itself 35 A context-aware profile can be learnt by combining two components in a linear fashion
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy :: WRI(u) C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) WRI(u) = ∑ di* r(u,i) MAXi=1 |L| NON-CONTEXTUAL USER PREFERENCES 36
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy :: WRI(u) C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) WRI(u) = ∑ di* r(u,i) MAXi=1 |L| items the user liked 37
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy :: WRI(u) C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) WRI(u) = ∑ di* r(u,i) MAXi=1 |L| vector-space representation of the item built by Semantic Content Analyzer 38
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy :: WRI(u) C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) WRI(u) = ∑ di* r(u,i) MAXi=1 |L| normalized rating 39
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy :: context(u,ck,vj) C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) context(u,ck,vj) = ∑ di* r(u,i,ck,vj) MAXi=1 |L(ck,vj)| Vector-space representation of the context 40
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) context(u,ck,vj) = ∑ di* r(u,i,ck,vj) MAXi=1 |L(ck,vj)| items the user liked in that specific context Context-aware User Profiler :: Strategy :: context(u,ck,vj) 41
  • r(u,i,ck,vj) Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) context(u,ck,vj) = ∑ di* MAXi=1 |L(ck,vj)| vector space representation of the item Context-aware User Profiler :: Strategy :: context(u,ck,vj) 42
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) context(u,ck,vj) = ∑ di* r(u,i,ck,vj) MAXi=1 |L(ck,vj)| normalized rating in that specific context Context-aware User Profiler :: Strategy :: context(u,ck,vj) 43
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) Ratio: context is just a factor which can influence user’s perception of an item 44
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy if the user did not express any preference in that specific contextual setting, context(u,ck,vj) = 0 ! —> non contextual recommendation C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) Ratio: context is just a factor which can influence user’s perception of an item 45 X
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Strategy Otherwise parameter α is exploited to tune a specific component of the formula Ratio: context is just a factor which can influence user’s perception of an item 46 C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj)
  • Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: How do we come to this formula? C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) 47
  • C-WRI(u,ck,vj) = α * WRI(u) + (1-α) * context(u,ck,vj) Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: How do we come to this formula? Insight: it exists a set of terms that is more descriptive of items relevant in that specific context for a romantic dinner, e.g. candlelight, seaview, violin 48 e.g. task = dinner, company=girlfriend
  • Context is represented on the ground of the items the user liked in that specific contextual setting Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Context-aware User Profiler :: Our formula inherits this insight 49 r(u,i,ck,vj) MAXi=1 |L(ck,vj)| context(u,ck,vj) = ∑ di*
  • Context is represented on the ground of the items the user liked in that specific contextual setting Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 50 r(u,i,ck,vj) MAX Items are represented on the ground of the co-occurrences between entities i=1 |L(ck,vj)| context(u,ck,vj) = ∑ di* Context-aware User Profiler :: Our formula inherits this insight
  • Context is represented on the ground of the items the user liked in that specific contextual setting Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 51 r(u,i,ck,vj) MAX Items are represented on the ground of the co-occurrences between entities i=1 |L(ck,vj)| context(u,ck,vj) = ∑ di* the resulting representation of the context is such that a bigger weight is given to the entities which typically occur in the description of the items relevant in that specific context Context-aware User Profiler :: Our formula inherits this insight
  • context(u,ck,vj) = ∑ di* Contextual eVSM Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 52 r(u,i,ck,vj) MAX Thanks to Distributional Semantics Models it is possible to build a vector-space representation of the context which emphasize the importance of those terms, since they are more used (—> more important) in that specific contextual setting. i=1 |L(ck,vj)| Context-aware User Profiler :: Our formula inherits this insight
  • Contextual eVSM Recommendation step Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Skyfall WRI(u) Austin Powers Up! The goal of our context-aware profiling strategy is to perturb the representation of user preferences and to provide him with context-aware recommendations 53 non-contextual preferences
  • Contextual eVSM Recommendation step Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Skyfall C-WRI(u) Austin Powers Up! The goal of our context-aware profiling strategy is to perturb the representation of user preferences and to provide him with context-aware recommendations 54 contextual preferences (e.g. company = friends)
  • Experimental Evaluation Research Hypothesis Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 55 1. Does C-eVSM outperform its non-contextual counterpart? 2. Does the novel representation based on entity linking and distributional semantics outperform a simple keyword- based one? 3. How does our model perform with respect to the current literature?
  • Experimental Evaluation Description of the dataset Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 56 • Movie recommendation! • Subset of IMDB data • 202 movies (textual features crawled from Wikipedia) • 62 users and 1457 ratings! • 4 contextual dimensions! • TIME (weekend, weekday) • PLACE (theather, home) • COMPANION (alone, friends, boyfriend, family) • MOVIE-RELATED (release week or not)
  • Experimental Evaluation Design of the Experiment Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 57 • Dataset and experimental settings replicate Adomavicius’ experiment (*)! • Evaluation over 9 different contextual settings! • Home, Friends, Non-release, Weekend, Weekday, GBFriends, TheatherWeekend and TheatherFriends • Metric: F1-Measure • Experimental protocol: bootstrapping! • 29/30th of the data as training • 1/30th as test • Randomly generated, 500 runs (*) G.Adomavicius et al. , Incorporating contextual information in recommender systems using a multi-dimensional approach.ACM Trans. Inf. Systems, 2005
  • Experimental Evaluation eVSM configurations Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 58 • Non-contextual baseline: eVSM! • WRI profiling strategy • WQN profiling strategy • Context-aware framework: C- eVSM! • C-WRI profiling strategy • C-WQN profiling strategy • Three values for parameter α! • 0.2 , 0.5, 0.8 8 configurations for each run
  • Experimental Evaluation eVSM configurations Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 59 • Non-contextual baseline: eVSM! • WRI profiling strategy • WQN profiling strategy • Context-aware framework: C- eVSM! • C-WRI profiling strategy • C-WQN profiling strategy • Three values for parameter α! • 0.2 , 0.5, 0.8 • WQN! • Alternative profiling strategy (*) • Models negative user feedbacks as well • Combines positive and negative preferences by means of a Quantum Negation (**) Operator (*) C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Random indexing and 
 negative user preferences for enhancing content-based recommender systems. In EC-Web 2011, volume 85 of LNBIP, pages 270–281. 2011. (**) D. Widdows. Orthogonal negation in vector spaces for modelling word- meanings and document retrieval. In ACL, pages 136–143, 2003.
  • Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 60 Comparison of C-eVSM vs eVSM (keyword-based)
  • Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 61 Selection of Results :: HOME segment WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 45 48,75 52,5 56,25 60 58,8 57,82 54,81 53,62 50,6 48,23 46,62 47,62 contextual eVSM improves the F1 measure
  • Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 62 Selection of Results :: HOME segment WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 45 48,75 52,5 56,25 60 58,8 57,82 54,81 53,62 50,6 48,23 46,62 47,62 contextual eVSM improves the F1 measure paired t-test (p<0.05) baseline baseline
  • Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 63 Selection of Results :: HOME segment WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 45 48,75 52,5 56,25 60 58,8 57,82 54,81 53,62 50,6 48,23 46,62 47,62 α=0.8 is better than α=0.5
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 42,0 45,3 48,5 51,8 55,0 54,39 50,04 45,93 53,18 50,11 50,54 44,91 49,43 Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 64 Selection of Results :: FRIENDS segment Similar outcomes: C-eVSM outperforms eVSM paired t-test (p<0.05)
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 42,0 45,3 48,5 51,8 55,0 54,39 50,04 45,93 53,18 50,11 50,54 44,91 49,43 Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 65 Selection of Results :: FRIENDS segment α=0.2 does not improve F1-measure
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 42,0 45,8 49,5 53,3 57,0 56,78 52,55 48,67 55,94 52,18 49,05 48,24 48,95 Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 66 Selection of Results :: NON-RELEASE segment C-WQN with α=0.8 is typically the best-performing configuration paired t-test (p<0.05)
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 42,0 45,8 49,5 53,3 57,0 56,78 52,55 48,67 55,94 52,18 49,05 48,24 48,95 Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 67 Selection of Results :: NON-RELEASE segment Outcome: context has just to slightly influence user preferences
  • Experiment 1 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 68 Outcomes • Contextual eVSM outperforms eVSM • 8 segments out of 9 • Little statistical significance • Negation is useful when dataset is well-balanced • Higher α values lead to a better F1 • Best-performing configurations are C-WQN-0.8 (4 times), C-WRI-0.8 (1 times), C-WRI-0.5 (3 times)
  • Experiment 2 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 69 Comparison of entity-based vs keyword-based content representation
  • Experiment 2 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 70 WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 40,0 47,5 55,0 62,5 70,0 61,30 61,96 54,81 57,53 56,75 56,38 46,62 56,13 58,80 57,82 53,37 53,62 50,60 48,23 44,56 47,62 Keywords Entities Selection of Results :: HOME segment Semantic representation improves F1 in all the configurations
  • Experiment 2 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 71 Selection of Results :: HOME segment Gaps are significant in 5 out of 8 configurations WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 40,0 47,5 55,0 62,5 70,0 61,3 61,96 54,81 57,53 56,75 56,38 46,62 56,13 58,80 57,82 53,37 53,62 50,60 48,23 44,56 47,62 Keywords Entities
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 40,0 47,5 55,0 62,5 70,0 61,3 61,96 54,81 57,53 56,75 56,38 46,62 56,13 58,80 57,82 53,37 53,62 50,60 48,23 44,56 47,62 Keywords Entities Experiment 2 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 72 Selection of Results :: HOME segment Again, higher α values lead to the best F1-measure scores
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 43,0 48,5 54,0 59,5 65,0 58,37 57,2 52,82 58,25 55,68 56,24 49,19 56,17 54,39 50,04 45,93 53,18 50,11 50,54 44,91 49,43 Keywords Entities Experiment 2 73 Selection of Results :: FRIEND segment Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 +6,42%improvement, gap always significant
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 43,0 48,5 54,0 59,5 65,0 58,37 57,2 52,82 58,25 55,68 56,24 49,19 56,17 54,39 50,04 45,93 53,18 50,11 50,54 44,91 49,43 Keywords Entities Experiment 2 74 Selection of Results :: FRIEND segment Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Negation+ α Higher values ➝ best configuration
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 43,0 48,5 54,0 59,5 65,0 62,16 57,81 54,72 56,45 58,11 57,21 55,82 56,34 52,64 51,40 46,65 50,71 52,87 53,95 52,79 50,91 Keywords Entities Experiment 2 75 Selection of Results :: THEATER segment Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Best perfoming segment: +6,49% improvement over keywords
  • WRI C-WRI-0.2 C-WRI-0.5 C-WRI-0.8 WQN C-WQN-0.2 C-WQN-0.5 C-WQN-0.8 43,0 48,5 54,0 59,5 65,0 62,16 57,81 54,72 56,45 58,11 57,21 55,82 56,34 52,64 51,40 46,65 50,71 52,87 53,95 52,79 50,91 Keywords Entities Experiment 2 76 Selection of Results :: THEATER segment Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 C-WQN is the best perfoming configuration: +9,52%
  • Experiment 2 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 77 Outcomes • Novel semantic representation outperforms the keyword-based one • 7 segments out of 9 • +4% on average, eanging from +1,34% to +6,49% • Important gaps in terms of F1-measure • Entity-based outperforms keywords in 65 segments out of 90 (72%) • Statistically significant gap in 52 out of 90 of the comparisons (58%) • Negation and higher α values lead to a better F1 • Best-performing configurations are C-WQN-0.8 (3 times), C-WQN-0.5 (2 times), C-WRI-0.5 (2 times)
  • Experiment 3 Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 78 Comparison to context-aware CF algorithm(*) (*) G.Adomavicius et al. , Incorporating contextual information in recommender systems using a multi- dimensional approach.ACM Trans. Inf. Systems, 2005
  • Home Friends Weekend Theater Nonrelease Weekday GBFriends Theater-Weekend Theater-Friends 35,0 43,8 52,5 61,3 70,0 60,7 64,1 48 37,9 43,2 60,8 54,2 48,2 39,19 55,96 54,95 50,72 48,02 57,01 61,16 60,39 58,37 61,96 c-eVSM CACF Experiment 3 79Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Comparison to context-aware CF algorithm Contextual eVSM overcomes CACF in 7 segments out of 9 ✔
  • Home Friends Weekend Theater Nonrelease Weekday GBFriends Theater-Weekend Theater-Friends 35,0 43,8 52,5 61,3 70,0 60,7 64,1 48 37,9 43,2 60,8 54,2 48,2 39,19 55,96 54,95 50,72 48,02 57,01 61,16 60,39 58,37 61,96 c-eVSM CACF Experiment 3 80Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Comparison to context-aware CF algorithm Gap is statistically significant in 5 segments out of 7
  • Recap 81Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014
  • Recap 82Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Contextual eVSM: context-aware recommendation framework Content Representation based on Distributional Semantics and Entity Linking Profile Learning based on a perturbation of non-contextual preferences with a semantic representation of the context! Experimental session confirmed the effectiveness of the framework as well as of the novel semantic representation! Framework overcomes a context-aware collaborative filtering baseline
  • Future Research 83Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014
  • Future Research 84Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis. Combining Distributional Semantics and Entity Linking for Context-aware Content-based Recommendations. UMAP 2014, Aalborg (Denmark), July 8, 2014 Evaluation against different datasets and stronger baselines; Exploitation of Linked Data and Open Knowledge Sources for content representation; Evaluation of Novelty, Diversity and Serendipity of the Recommendations;
  • questions? Cataldo Musto, Ph.D cataldo.musto@uniba.it