Abstract
Recommendation system is an intelligent business platform which bases on users’ interests and historical purchase behavior to recommend users the information and commodities that they are interested. One challenge problem in current recommendation system is the sparse problem of dataset. Users usually evaluate a few items on website, which result in extremely sparse dataset and low-quality recommendation. Though solutions such as average value filling and genetic clustering are formulated, poor recommendatory efficiency and accuracy exist as before when users’ rating dataset is extremely sparse. A new method called CF-average filling algorithm is proposed in this paper to optimize the problem of data sparsity. And three kinds of clustering algorithms that include k-means clustering, hierarchical clustering and spectral clustering are used for community discovery on real dataset from the MovieLens to evaluate our CF-averaging filling algorithm. Experimental results show that the proposed algorithm is highly effective and generally applicable to solve the problem of data sparsity in personalized recommendation system.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (61472389) and the National Key Technology R&D Program of China under Contract (2015BAK22B02).
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Dong, M., Zhang, Y., Yan, J. (2018). Research on Sparse Problem of Personalized Recommendation System. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_30
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DOI: https://doi.org/10.1007/978-981-10-8108-8_30
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