Abstract
Cross domain recommendation which aims to transfer knowledge from auxiliary domains to target domains has become an important way to solve the problems of data sparsity and cold start in recommendation systems. However, most existing works only consider ratings and tags, but ignore the text information like reviews. In reality, review text in some way well explains the reason why a product could gain such high or low ratings and reflect users’ sentiment towards different aspects of an item. For instance, reviews can be taken advantage to obtain users’ attitudes towards the specific aspect “screen” or “battery” of a “cell phone”. Taking these aspect factors into cross domain recommendation will bring us more about user preference, and thus could potentially improve the performance of recommendation. In this paper, we for the first time study how to fully exploit the aspect factors extracted from the review text to improve the performance of cross domain recommendation. Specifically, we first model each user’s sentiment orientation and concern degree towards different aspects of items extracted from reviews as tensors. To effectively transfer the aspect-level preferences of users towards items, we propose a joint tensor factorization model on auxiliary domain and target domain together. Experimental results on real data sets show the superior performance of the proposed method especially in the cold-start users in target domain by comparison with several state-of-the-arts cross domain recommendation methods.
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Acknowledgements
This work is supported by NSF of China (No. 61602237), 973 Program (No. 2015CB352501), NSF of Shandong, China (No. ZR2013FQ009), the Science and Technology Development Plan of Shandong, China (No. 2014GGX101047, No. 2014GGX101019). This work is also supported by US NSF grants III-1526499, and CNS-1115234.
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Song, T., Peng, Z., Wang, S., Fu, W., Hong, X., Yu, P.S. (2017). Review-Based Cross-Domain Recommendation Through Joint Tensor Factorization. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_33
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