A multimedia recommender integrating object features and user behavior

Despite the great amount of work done in the last decade, retrieving information of interest from a large multimedia repository still remains an open issue. In this paper, we propose an intelligent browsing system based on a novel recommendation paradigm. Our approach combines usage patters with low-level features and semantic descriptors in order to predict users’ behavior and provide effective recommendations. The proposed paradigm is very general and can be applied to any type of multimedia data. In order to make the recommender system even more flexible, we introduce the concept of multichannel browser, i.e. a browser that allows concurrent browsing of multiple media channels. We implemented a prototype of the proposed system and tested the effectiveness of our approach in a virtual museum scenario. Experimental results have proved that the system greatly enhances users’ experience, thus encouraging further research in this direction.

[1]  Jun Wang,et al.  Unified relevance models for rating prediction in collaborative filtering , 2008, TOIS.

[2]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[3]  Peretz Shoval,et al.  Evaluation of an ontology-content based filtering method for a personalized newspaper , 2008, RecSys '08.

[4]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[5]  Lars Schmidt-Thieme,et al.  Tag-aware recommender systems by fusion of collaborative filtering algorithms , 2008, SAC '08.

[6]  David McLean,et al.  An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources , 2003, IEEE Trans. Knowl. Data Eng..

[7]  Stephen W. Smoliar,et al.  Video parsing, retrieval and browsing: an integrated and content-based solution , 1997, MULTIMEDIA '95.

[8]  S. Santini.  Evaluation Vademecum for Visual information Systems∗ , 2000 .

[9]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[10]  Anh Duc Duong,et al.  Addressing cold-start problem in recommendation systems , 2008, ICUIMC '08.

[11]  Thomas Hofmann,et al.  Unifying collaborative and content-based filtering , 2004, ICML.

[12]  Gloria Bordogna,et al.  A multi-criteria content-based filtering system , 2007, SIGIR.

[13]  Boon-Lock Yeo,et al.  Video visualization for compact presentation and fast browsing of pictorial content , 1997, IEEE Trans. Circuits Syst. Video Technol..

[14]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[15]  Ahmed K. Elmagarmid,et al.  InsightVideo: toward hierarchical video content organization for efficient browsing, summarization and retrieval , 2005, IEEE Transactions on Multimedia.

[16]  George Lekakos,et al.  A hybrid approach for movie recommendation , 2006, Multimedia Tools and Applications.

[17]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[18]  Shogo Nishida,et al.  Content-based music filtering system with editable user profile , 2006, SAC.

[19]  Mary Shapcott,et al.  Generating semantically enriched user profiles for Web personalization , 2007, TOIT.

[20]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[21]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Antonio d'Acierno,et al.  A recommendation system for browsing digital libraries , 2009, SAC '09.

[23]  David M. Pennock,et al.  Applying collaborative filtering techniques to movie search for better ranking and browsing , 2007, KDD '07.

[24]  Luo Si,et al.  Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model , 2004, CIKM '04.

[25]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[26]  V. S. Subrahmanian,et al.  Story creation from heterogeneous data sources , 2007, Multimedia Tools and Applications.

[27]  Simone Santini,et al.  Evaluation vademecum for visual information system , 1999, Electronic Imaging.

[28]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .