Abstract
Cross-domain algorithms which aim to transfer knowledge available in the source domains to the target domain are gradually becoming more attractive as an effective approach to help improving quality of recommendations and to alleviate the problems of cold-start and data sparsity in recommendation systems. However, existing works on cross-domain algorithm mostly consider ratings, tags and the text information like reviews, cannot use the sentiments implicated in the reviews efficiently. In this paper, we propose a Sentiment Review Pattern Mapping framework for cross-domain recommendation, called SRPM. The proposed SRPM framework can model the semantic orientation of the reviews of users, and transfer sentiment review pattern of users by using a multi-layer perceptron to capture the nonlinear mapping function across domains. We evaluate and compare our framework on a set of Amazon datasets. Extensive experiments on each cross-domain recommendation scenarios are conducted to prove the high accuracy of our proposed SRPM framework.
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Acknowledgments
This work is supported by NSF of Shandong, China (Nos. ZR2017MF065, ZR2018MF014), the Science and Technology Development Plan Project of Shandong, China (No. 2016GGX101034).
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Xu, Y., Peng, Z., Hu, Y., Hong, X., Fu, W. (2018). Cross-Domain Recommendation for Mapping Sentiment Review Pattern. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_29
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DOI: https://doi.org/10.1007/978-3-319-99365-2_29
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