A Recommender System Based on Content Clustering Used to Propose Forum Articles

Nowadays, WWW services compete intensively to attract attention of visitors. They search new solutions to increase attractiveness of the systems and to satisfy all customer expectations. To achieve this they often offer an individual approach to each user, e.g. applying recommender systems. A recommender system is able to learn a customer’s preferences and recommend products, which the user is probably interested in. The recommendations are based on similarity between registered users’ activity, e.g. items, which they visited or bought.

[1]  Xiangliang Zhang,et al.  Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information , 2011, IFIPTM.

[2]  Bamshad Mobasher,et al.  Intelligent Techniques for Web Personalization , 2005, Lecture Notes in Computer Science.

[3]  Shichao Zhang,et al.  AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings , 2005, Australian Conference on Artificial Intelligence.

[4]  Ana L. N. Fred,et al.  Agents and Artificial Intelligence , 2013 .

[5]  Choochart Haruechaiyasak,et al.  A dynamic framework for maintaining customer profiles in e-commerce recommender systems , 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service.

[6]  Sung-Bong Yang,et al.  An Effective Recommendation Algorithm for Clustering-Based Recommender Systems , 2005, Australian Conference on Artificial Intelligence.

[7]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[8]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[9]  Urszula Kuzelewska.  Advantages of Information Granulation in Clustering Algorithms , 2011, ICAART.

[10]  Naranker Dulay,et al.  Longitude: A Privacy-Preserving Location Sharing Protocol for Mobile Applications , 2011, IFIPTM.

[11]  Sean M. McNee,et al.  Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems , 2006 .

[12]  Ali Selamat,et al.  A scalable collaborative recommender algorithm based on user density-based clustering , 2011, The 3rd International Conference on Data Mining and Intelligent Information Technology Applications.

[13]  Damianos Gavalas,et al.  A web-based pervasive recommendation system for mobile tourist guides , 2011, Personal and Ubiquitous Computing.

[14]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[15]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .