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Hybrid Semantics aware Recommendations Exploiting Knowledge Graph Embeddings

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Hybrid Semantics-aware Recommendations Exploiting
Knowledge Graph Embeddings - AI*IA 2019 - Conferenza Italiana di Intelligenza Artificiale

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Hybrid Semantics aware Recommendations Exploiting Knowledge Graph Embeddings

  1. 1. @cataldomusto cataldo.musto@uniba.it Hybrid Semantics-aware Recommendations Exploiting Knowledge Graph Embeddings CATALDO MUSTO, PIERPAOLO BASILE AND GIOVANNI SEMERARO UNIVERSITÀ DEGLI STUDI DI BARI ALDO MORO - ITALY
  2. 2. Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019 2 WHAT ARE WE TALKING ABOUT?
  3. 3. Recommender Systems 3 Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  4. 4. Graph-based Data Model Very popular representation for hybrid recommender systems Recommendations are obtained by running algorithms such as PageRank and Spreading Activation Very good performance in recommendation tasks [*] 4 [*] Cataldo Musto, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro: Introducing linked open data in graph-based recommender systems. Inf. Process. Manage. 53(2): 405-435 (2017) Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019 Users = nodes Items = nodes Characteristics of the items = nodes from Preferences & Properties= edges
  5. 5. Intuition: Graph Embedding techniques 5 Graph embedding techniques take a graph as input and build a vector-space representation of the nodes (and the relations, eventually) as output. They resulted as very effective in several machine learning tasks. What about a recommender system based on graph embedding techniques? Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  6. 6. Research Questions 6 How effective is a hybrid recommendation method based on graph embedding techniques? How do features extracted from DBpedia impact on the overall effectiveness of the representation? 1. 2. Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  7. 7. 7 METHODOLOGY Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  8. 8. Methodology 8 Step 1 – build a graph-based data model. Two alternatives: bipartite (without DBpedia features ) or tripartite (with DBpedia features) Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  9. 9. Methodology 9 Step 2 – run a graph embedding technique over the data model, and build a vector space representation for each user and item Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  10. 10. Methodology 10 Step 3 – use the vectors to feed a classification algorithm in a node classification task, that labels the nodes (items) as positive or negative for the user ? Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  11. 11. 11 EXPERIMENTS Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  12. 12. Experimental Design 12 Datasets: ML1M - Librarythings – Last.fm Data Model: collaborative, DBpedia- based, complete DBpedia features: all Methodology - Step 1 Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  13. 13. Experimental Design 13 Graph Embeddings Techniques: Laplacian Eigenmaps (a community- preserving technique) and Node2Vec (a structural- preserving technique) Size of the Vectors: 128, 256, 512 Methodology - Step 2 Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  14. 14. (Recap of Graph Embedding techniques) 14 Community-preserving technique (e.g., Laplacian Eigenmaps) Structural-preserving technique (e.g., Node2Vec) Input Output Source: Goyal, Palash, and Emilio Ferrara. "Graph embedding techniques, applications, and performance: A survey." Knowledge-Based Systems 151 (2018): 78-94. Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  15. 15. Experimental Design 15 Classification Algorithm: Logistic Regression Metrics: F1-measure Methodology - Step 3 ? 3 datasets x 2 techniques x 3 embedding size x 3 data models = 54 runs Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  16. 16. Results 16 1. Node2Vec > Laplacian Eigenmaps 2. Complete (tripartite) Graph > Collaborative (bipartite) Graph 3. Tiny gaps by increasing the size of the vectors Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  17. 17. Results Our framework overcomes all the baselines 0,6886 0,6559 0,6117 0,597 0,5935 0,5115 0,5835 0,5935 0,5092 0,5297 0,5961 0,5117 0,6023 0,6034 0,599 0,6032 0,5964 0,5642 0,6083 0,6152 0,6107 0,5 0,55 0,6 0,65 0,7 MovieLens Librarything Last.fm GE+LOD U2U-KNN I2I-KNN BPRMF PR BPRMF+LOD PPR+LOD U2U-KNN: User-to-User Collaborative Filtering I2I-KNN: Item-to-Item Collaborative Filtering BPRMF: Bayesian Personalized Ranking Matrix Factorization PPR: Personalized PageRank Advanced baselinesSimple baselines DBpedia-based baselines 17Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  18. 18. Take-home Messages 1. 2. Graph embedding techniques can be an effective alternative to classic graph-based data models. Structural-preserving techniques tend to obtain better results. 18 Exogenous descriptive features extracted from knowledge graphs as DBpedia further improve the accuracy of the model. The size of the vectors does not significantly affects the overall effectiveness of the recommendation methodology 3. Improvement over state-of-art recommendation methods! Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019
  19. 19. Cataldo Musto, Pierpaolo Basile, Giovanni Semeraro. Hybrid Semantics-aware Recommendation Exploiting Knowledge Graph Embeddings. AI*IA 2019, 18th International Conference of the Italian Association for Artificial Intelligence, Rende, November 19, 2019 19 Grazie! cataldo.musto@uniba.it @cataldomusto Contacts

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