A Generic Semantic-based Framework for Cross-domain Recommendation - PowerPoint PPT Presentation

A Generic Semantic-based Framework for Cross-domain Recommendation Ignacio Fernández-Tobías 1 , Marius Kaminskas 2 , Iván Cantador 1 , Francesco Ricci 2 1 Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain ign.fernandez01@estudiante.uam.es, ivan.cantador@uam.es 2 Faculty of Computer Science, Free University of Bozen-Bolzano, Italy mkaminskas@unibz.it, fricci@unibz.it

Contents 1 • Cross-domain recommendation • Case study: adapting music recommendation to points of interest • A semantic-based framework for cross-domain recommendation • Semantic-based knowledge representation • Semantic graph-based recommendation algorithm • Preliminary results • Future work A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Contents 2 • Cross-domain recommendation • Case study: adapting music recommendation to points of interest • A semantic-based framework for cross-domain recommendation • Semantic-based knowledge representation • Semantic graph-based recommendation algorithm • Preliminary results • Future work A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Cross-domain recommendation 3 • Recommender systems can help users to make choices, by proactively finding relevant items or services, taking into account or predicting the users’ tastes, priorities and goals • The vast majority of the currently available recommender systems predict the user’s relevance of items in a specific and limited domain A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Cross-domain recommendation 4 • In some applications, it could be useful to offer the user joint personalized recommendations of items belonging to multiple domains • In an e-commerce site, we may suggest movies or videogames based on a particular book bought by a costumer • In a travel application, we may suggest cultural events may interest a person who has booked a hotel in a particular place • In an e-learning system, we may suggest educational websites with topics related to a video documentary a student has seen • Potential benefits • Offering diversity and serendipity • Addressing the user cold-start problem (on the target domain) • Mitigating the sparsity problem A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Cross-domain recommendation 5 • Some real applications do already recommend items from different domains, but • their recommendations rely on statistical analysis of popular items, without any personalization strategy, or • most of them only exploit information about the user preferences available in the target domain A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Cross-domain recommendation 6 • Research questions [Winoto & Tang, 2008] 1. At community level, are there correlations between user preferences for items belonging to the different domains of interest? 2. At individual level, can we build a recommendation model where each user’s preferences in source domains are used to predict/adapt her preferences in target domains? 3. How should we evaluate the effectiveness of cross-domain item recommendations? [Winoto & Tang, 2008] Winoto, P., Tang, T. 2008. If You Like the Devil Wears Prada the Book, Will You also Enjoy the Devil Wears Prada the Movie? A Study of Cross-Domain Recommendations. New Generation Computing 26(3), 209-225. A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Contents 7 • Cross-domain recommendation • Case study: adapting music recommendation to points of interest • A semantic-based framework for cross-domain recommendation • Semantic-based knowledge representation • Semantic graph-based recommendation algorithm • Preliminary results • Future work A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Case study: adapting music recommendation to points of interest 8 • Recommending music artists that suit places of interest (POIs) • Mobile city guide soundtrack • Adaptive music playlist in a car [Braunhofer et al., 2011] Braunhofer, M., Kaminskas, M., Ricci, F. 2011. Recommending Music for Places of Interest in a Mobile Travel Guide. 5th ACM Conference on Recommender Systems. A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Case study: adapting music recommendation to points of interest 9 • In a previous work [Kaminskas & Ricci, 2011], emotional tags were used to manually annotate places and music • Emotional tags can be used to find matching between music and places of interest ‐ e.g. a monument and a music track may be described as ‘strong’ and ‘triumphant’ [Kaminskas & Ricci, 2011] Kaminskas, M., Ricci, F. 2011. Location-Adapted Music Recommendation Using Tags. 19th International Conference on User Modeling, Adaptation and Personalization , 183-194. A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Case study: adapting music recommendation to points of interest 10 • In this work, we aim at automatically finding semantic relations between POIs and music artists • We propose to explore the Web of Data (Linked Data) to find such relations • Specifically, we propose to exploit DBpedia , the Linked Data version of Wikipedia • DBpedia can be considered as a core ontology in the Web of Data • Connected to many other ontologies • Describing and linking more than 3.5 million concepts from a large variety of knowledge domains A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Case study: adapting music recommendation to points of interest 11 • In this work, we aim at automatically finding semantic relations between POIs and music artists • We propose to explore the Web of Data (Linked Data) to find such relations • Specifically, we propose to exploit DBpedia , the Linked Data version of Wikipedia • DBpedia can be considered as a core ontology in the Web of Data • Connected to many other ontologies • Describing and linking more than 3.5 million concepts from a large variety of knowledge domains A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Case study: adapting music recommendation to points of interest 12 • Issues to investigate, identified in [Winoto & Tang, 2008] 1. Correlations between user preferences for items of the different domains Correlations between POIs and music were established through tags in  [Kaminskas & Ricci, 2011] 2. Recommendation model to predict/adapt user preferences across domains  This paper addresses this particular issue, presenting a semantic-based framework to support cross-domain recommendation 3. Evaluation of cross-domain recommendation effectiveness Future work  A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

Contents 13 • Cross-domain recommendation • Case study: adapting music recommendation to points of interest • A semantic-based framework for cross-domain recommendation • Semantic-based knowledge representation • Semantic graph-based recommendation algorithm • Preliminary results • Future work A Generic Semantic-based Framework for Cross-domain Recommendation 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011