An Effective Hybrid Collaborative Recommendation Algorithm for Alleviating Data Sparsity

Abstract:

Every day there is lots of information obtained via the Internet. The problem of information overload is becoming increasingly serious, and we have all experienced the feeling of being overwhelmed. Many researchers and practitioners more attention on building a suitable tool that can help users conserve resources and services that are wanted. Personalized recommendation systems are used to make recommendations for the user invisible elements get to their preferences, which differ in the position, a user from one another in order to provide information based. The paper presented a personalized recommendation approach joins item feature technology and self-organizing map technology. It used the item feature to fill the vacant where necessary, which employing the collaborative recommendation. And then, the presented approach utilized the user based collaborative recommendation to produce the recommendations, which employing the self-organizing map clustering. The recommendation joining item feature and self-organizing map can alleviate the data sparsity problem in the collaborative recommendations.

• Periodical: Applied Mechanics and Materials (Volume 39)
• Pages: 535-539
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.39.535
• Online since: November 2010
• Authors: Guang Hua Cheng
• Keywords: Collaborative Recommendation, Data Sparsity, Item Feature, Self-Organizing Maps

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