A semantic approach to improve neighborhood formation in collaborative recommender systems

Automatic recommenders are now omnipresent in e-commerce websites, as selecting and offering to users products they may be interested in can considerably increase sales revenue. The most popular recommendation strategy is currently considered to be the collaborative filtering technique, based on offering to the user who will receive the recommendation items that were appealing to other individuals with similar preferences (the so-called neighbors). On the other hand, its principal obstacle is the sparsity problem, related to the difficulty to find overlappings in ratings when there are many items. As the product catalogue of these sites gets more and more diverse, a new problem has arisen that happens when users share likings for lots of products (for which they are reckoned to be neighbors) but they differ in products similar to the one that is being considered for recommendation. They are fake neighbors. Narrowing the range of products on which similarities between users are sought can help to avoid this, but it usually causes a worsening of the sparsity problem because the chances of finding overlappings gets lower. In this paper, a new strategy is proposed based on semantic reasoning that aims to improve the neighborhood formation in order to overcome the aforementioned fake neighborhood problem. Our approach is aimed at making more flexible the search for semantic similarities between different products, and thus not require users to rate the same products in order to be compared.

[1]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

[2]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[3]  Fuyuki Ishikawa,et al.  Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[4]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[5]  Rafael Valencia-García,et al.  Solving the cold-start problem in recommender systems with social tags , 2010, Expert Syst. Appl..

[6]  Hsinchun Chen,et al.  A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce , 2007, IEEE Intelligent Systems.

[7]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[8]  Enrique Herrera-Viedma,et al.  A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office , 2012, Inf. Sci..

[9]  Chanle Wu,et al.  Solving the Sparsity Problem in Recommender Systems Using Association Retrieval , 2011, J. Comput..

[10]  Philip S. Yu,et al.  Music Recommendation Using Content and Context Information Mining , 2010, IEEE Intelligent Systems.

[11]  Fan-Sheng Kong,et al.  Semantic-Enhanced Personalized Recommender System , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[12]  Georg Lausen,et al.  On exploiting classification taxonomies in recommender systems , 2008, AI Commun..

[13]  Alberto Gil-Solla,et al.  Semantic inference of user's reputation and expertise to improve collaborative recommendations , 2012, Expert Syst. Appl..

[14]  José Juan Pazos-Arias,et al.  AVATAR: an improved solution for personalized TV based on semantic inference , 2006, IEEE Transactions on Consumer Electronics.

[15]  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.

[16]  Murat Göksedef,et al.  Combination of Web page recommender systems , 2010, Expert Syst. Appl..

[17]  Neil J. Hurley,et al.  An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering , 2004, Artificial Intelligence Review.

[18]  Wujian Yang,et al.  An improved collaborative filtering method for recommendations' generation , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[19]  Jorge García Duque,et al.  MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning , 2010, Knowledge and Information Systems.

[20]  Hsinchun Chen,et al.  A comparison of collaborative-filtering algorithms for ecommerce , 2007 .

[21]  Amit P. Sheth,et al.  Ρ-Queries: enabling querying for semantic associations on the semantic web , 2003, WWW '03.

[22]  Antonio Hernando,et al.  Collaborative filtering adapted to recommender systems of e-learning , 2009, Knowl. Based Syst..

[23]  Jorge García Duque,et al.  Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study , 2008, J. Syst. Softw..

[24]  Yu Li,et al.  A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce , 2005, Expert Syst. Appl..

[25]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.

[26]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

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

[28]  Wolfgang Wörndl,et al.  Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones , 2013, J. Syst. Archit..

[29]  Stuart Edward Middleton,et al.  Capturing knowledge of user preferences with recommender systems , 2003 .

[30]  Charalampos Konstantopoulos,et al.  Mobile recommender systems in tourism , 2014, J. Netw. Comput. Appl..