Abstract
Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems.
Programs Supported by Ningbo Natural Science Foundation (Grant No. 2010A610118)
A Project Supported by Ningbo Training Base of Textile and Fashion (Grant No. JD090312).
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Ying, Y. (2011). The Personalized Recommendation Algorithm Based on Item Semantic Similarity.. In: Ma, M. (eds) Communication Systems and Information Technology. Lecture Notes in Electrical Engineering, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21762-3_132
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DOI: https://doi.org/10.1007/978-3-642-21762-3_132
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