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 improve 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, and don’t take advantage of the sentiments implicated in the reviews efficiently, especially the negative sentiment information which is easy to be weakened during the process of transferring. In this paper, we propose a sentiment-aware review feature mapping framework for cross-domain recommendation, called SARFM. The proposed SARFM framework applies deep learning algorithm SDAE (Stacked Denoising Autoencoders) to model the Sentiment-Aware Review Feature (SARF) of users, and transfers SARF via 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 SARFM 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. (2018). SARFM: A Sentiment-Aware Review Feature Mapping Approach for Cross-Domain Recommendation. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_1
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