Abstract
A recommender system becomes more and more popular in e-commerce. Usually prediction results cannot satisfy users’ requirements fully, and sometimes it even contains totally irrelevant items. To reflect users’ newest preference and increase the quality of recommendation, a fast interactive item-based collaborative filtering algorithm is proposed. Firstly, we propose an item-based collaborative filtering algorithm with less time and space complexity. Then we introduce interactive iterations to reflect users’ up-to-date preference and increase users’ satisfaction. The experiments show that our fast interactive item-based CF algorithm has better recall and precision than traditional item-based CF algorithm.
Keywords
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Ji, Z., Zhang, Z., Zhou, C., Wang, H. (2017). A Fast Interactive Item-Based Collaborative Filtering Algorithm. In: Du, D., Li, L., Zhu, E., He, K. (eds) Theoretical Computer Science. NCTCS 2017. Communications in Computer and Information Science, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-10-6893-5_19
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DOI: https://doi.org/10.1007/978-981-10-6893-5_19
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