Hybrid system for personalized recommendations

Recommender systems are an important research area due to the various expansion possibilities that enhance the quality of the recommendations. A possible approach to improve the performance is to combine different recommendation techniques in a hybrid system that benefits from their complementarity and strengths. Our goal is to combine case-based reasoning and collaborative filtering to implement a scalable and domain-independent recommender system. The case-based reasoning engine will represent the core module of the system and will use the records of previous similar experiences to make suggestions or create new items to recommend. The collaborative filtering engine will be mainly used to adapt the recommendations to the preferences of the users and ensure a degree of diversity and novelty in the suggested items. Although the system needs to use the domain knowledge to generate personalized recommendations, it must be designed in a domain-independent way in order to make it adaptable to any application. In this paper, we present the global architecture of our hybrid recommender system and the ontology-based reasoning approach that will allow us to overcome the constraint of domain-independence.

[1]  Sean Breen,et al.  Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology , 1999 .

[2]  Mohd Shahizan Othman,et al.  Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study , 2011, ArXiv.

[3]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[4]  Barry Smyth,et al.  Personalized Electronic Program Guides for Digital TV , 2001, AI Mag..

[5]  Francesco Ricci,et al.  DIETORECS: Travel Advisory for Multiple Decision Styles , 2003, ENTER.

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

[7]  Freddy Lécué,et al.  Combining Collaborative Filtering and Semantic Content-Based Approaches to Recommend Web Services , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[8]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[9]  Dimitris Plexousakis,et al.  Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences , 2005, iTrust.

[10]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[11]  Barry Smyth,et al.  Case-based recommender systems , 2005, The Knowledge Engineering Review.

[12]  K. K. Bharadwaj,et al.  A Hybrid Recommender System Using Rule-Based and Case-Based Reasoning , 2022 .

[13]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[14]  Pedro A. González-Calero,et al.  An Architecture for Knowledge Intensive CBR Systems , 2000, EWCBR.

[15]  Stefan Wess,et al.  Case-Based Reasoning Technology: From Foundations to Applications , 1998, Lecture Notes in Computer Science.

[16]  Robin Cohen,et al.  Hybrid Recommender Systems for Electronic Commerce , 2000 .

[17]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[18]  Markus Zanker,et al.  A collaborative constraint-based meta-level recommender , 2008, RecSys '08.

[19]  Barry Smyth,et al.  Case-Based Recommendation , 2007, The Adaptive Web.

[20]  William W. Cohen,et al.  Community-Based Recommendations: a Solution to the Cold Start Problem , 2011 .

[21]  S.S.R. Abidi,et al.  Case Based Reasoning for Information Personalization: Using a Context-Sensitive Compositional Case Adaptation Approach , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[22]  Janet L. Kolodner,et al.  An introduction to case-based reasoning , 1992, Artificial Intelligence Review.

[23]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[24]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[25]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[26]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.