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CRAS: cross-domain recommendation via aspect-level sentiment extraction

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Abstract

To address the problem of sparse data and cold-start when facing new users and items in the single-domain recommendation, cross-domain recommendation has gradually become a hot topic in the recommendation system. This method enhances target domain recommendation performance by incorporating relevant information from an auxiliary domain. A critical aspect of cross-domain recommendation is the effective transfer of user preferences from the source to the target domain. This paper proposes a novel cross-domain recommendation framework, namely the Cross-domain Recommendation based on Aspect-level Sentiment extraction (CRAS). CRAS leverages user and item review texts in cross-domain recommendations to extract detailed user preferences. Specifically, the Biterm Topic Model (BTM) is utilized for the precise extraction of ’aspects’ from users and items, focusing on identifying characteristics that align with user interests and the positive attributes of items. These ’aspects’ represent distinct, influential features of the items. For example, a good service attitude can be regarded as a good aspect of a restaurant. Furthermore, this study employs an improved Cycle-Consistent Generative Adversarial Networks (CycleGAN), efficiently mapping user preferences from one domain to another, thereby enhancing the accuracy and personalization of the recommendations. Lastly, this paper compares the CRAS model with a series of state-of-the-art baseline methods in the Amazon review dataset, and experiment results show that the proposed model outperforms the baseline methods.

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Notes

  1. http://jmcauley.ucsd.edu/data/amazon/.

  2. https://www.nltk.org/.

  3. https://dumps.wikimedia.org/enwiki/20140102/.

  4. https://catalog.ldc.upenn.edu/LDC2011T07.

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Acknowledgements

This work was supported in part by Beijing Natural Science Foundation(Grant No.L233034), in part by the Open Project of Xiangjiang Laboratory (No.23XJ03006), in part by SMP-IDATA Open Youth Fund (No.SMP2023-iData-005), in part by the National Natural Science Foundation of China (Grant No.72274022, No.82071171), in part by Open Project (2023B02) of Guangxi Colleges and Universities Key Laboratory of Intelligent Software, in part by CCF-Zhipu AI Large Model Fund (Grant No. CCF-Zhipu202317), in part by Zhejiang Lab Open Research Project (Grant No.K2022KG0AB03) and in part by the Open Projects of the Technology Innovation Center of Cultural Tourism Big Data of Hebei Province (Grant No.SG2019036-zd202205).

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Authors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Fan Zhang, Yaoyao Zhou, Pengfei Sun, Yi Xu, Wanjiang Han & Jinpeng Chen

  2. Guangxi Colleges and Universities Key Laboratory of Intelligent Software, Wuzhou University, Wuzhou, 543002, China

    Fan Zhang & Hongben Huang

  3. Xiangjiang Laboratory, Changsha, 410205, China

    Jinpeng Chen

  4. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, 100876, China

    Fan Zhang, Yaoyao Zhou, Yi Xu, Wanjiang Han & Jinpeng Chen

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  1. Fan Zhang
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hongben Huang, Wanjiang Han, Yi Xu and Pengfei Sun. Fan Zhang, Yaoyao Zhou and Jinpeng Chen wrote the main manuscript text and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jinpeng Chen.

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Zhang, F., Zhou, Y., Sun, P. et al. CRAS: cross-domain recommendation via aspect-level sentiment extraction. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02130-6

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