Abstract
Matrix Factorization is a useful approach in recommender systems. However, it only considers the user-item matrix, which will result in the data sparsity problem. To remit this issue, most researchers focus on using the item side-information to improve the performance and make a great success such as CDL, ConvMF. But these models all ignore the effect of specific item bias which is important because the same word represented different semantic for the different item. For example, the word “long” is a good description of the battery renewal time but opposite for the logistics of an item. In our work, we present a hybrid model that integrates the textual bias and rating bias to the PMF framework simultaneously. This model can exploit and modified the item specific word representation by CNN and obtain more precise side-information. Experiments on the three real-world datasets show that our model outperforms state-of-the-art method.
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This work was supported in part by National Key R&D Program of China under Grant 2017YFB101000.
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Dai, J., Li, M., Hu, S., Han, J. (2018). A Hybrid Model Based on the Rating Bias and Textual Bias for Recommender Systems. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_18
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DOI: https://doi.org/10.1007/978-3-030-04179-3_18
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