Integrated mobile content recommendation: A comparison study

A recommendation system can be used to help mobile device users for content filtering. However, there are problems related to sparsity of information from a first-time user. The problem is also regarding to initial rating of the content in an early stage of the system. Therefore, mobile content filtering is necessary for user to obtain personalised content delivery. This paper proposes the integrated mobile content recommendation method by combining classification and association rule techniques to establish model for new users and first rater on mobile content. The model also enhances the recommendation system in an early stage by recommending relevant items. The experiment has shown that the integrated method can perform better than the other compared methods. This can address the problem of sparsity for mobile content recommendation systems.

[1]  Chan Young Kim,et al.  VISCORS: a visual-content recommender for the mobile Web , 2004, IEEE Intelligent Systems.

[2]  Félix Hernández-del-Olmo,et al.  Evaluation of recommender systems: A new approach , 2008, Expert Syst. Appl..

[3]  Sung-Bae Cho,et al.  Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices , 2007, UIC.

[4]  Duen-Ren Liu,et al.  Mobile commerce product recommendations based on hybrid multiple channels , 2011, Electron. Commer. Res. Appl..

[5]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[6]  Duen-Ren Liu,et al.  A hybrid of sequential rules and collaborative filtering for product recommendation , 2009, Inf. Sci..

[7]  Alexander Zipf,et al.  Implementing adaptive mobile GI services based on ontologies: Examples from pedestrian navigation support , 2006, Comput. Environ. Urban Syst..

[8]  Bradley N. Miller,et al.  MovieLens Unplugged: Experiences with a Recommender System on Four Mobile Devices , 2004 .

[9]  Sung Joo Park,et al.  MONERS: A news recommender for the mobile web , 2007, Expert Syst. Appl..

[10]  Liang-Chu Chen,et al.  Building and evaluating a location-based service recommendation system with a preference adjustment mechanism , 2009, Expert Syst. Appl..

[11]  So Young Sohn,et al.  Searching customer patterns of mobile service using clustering and quantitative association rule , 2008, Expert Syst. Appl..

[12]  Dimitris Plexousakis,et al.  Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents , 2005, Eng. Appl. Artif. Intell..

[13]  Lance Chun Che Fung,et al.  A model for mobile content filtering on non-interactive recommendation systems , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[14]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[15]  Chen Xinmeng,et al.  Collaborative filtering algorithms based on Kendall correlation in recommender systems , 2008, Wuhan University Journal of Natural Sciences.