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Title: Review-Based Cross-Domain Collaborative Filtering: A Neural Framework
Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD.  more » « less
Award ID(s):
1755910
NSF-PAR ID:
10187028
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the Third Workshop on Recommendation in Complex Scenarios
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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