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

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.