Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives
arxiv(2024)
摘要
Recommender systems play a pivotal role in helping users navigate an
overwhelming selection of products and services. On online platforms, users
have the opportunity to share feedback in various modes, including numerical
ratings, textual reviews, and likes/dislikes. Traditional recommendation
systems rely on users explicit ratings or implicit interactions (e.g. likes,
clicks, shares, saves) to learn user preferences and item characteristics.
Beyond these numerical ratings, textual reviews provide insights into users
fine-grained preferences and item features. Analyzing these reviews is crucial
for enhancing the performance and interpretability of personalized
recommendation results. In recent years, review-based recommender systems have
emerged as a significant sub-field in this domain. In this paper, we provide a
comprehensive overview of the developments in review-based recommender systems
over recent years, highlighting the importance of reviews in recommender
systems, as well as the challenges associated with extracting features from
reviews and integrating them into ratings. Specifically, we present a
categorization of these systems and summarize the state-of-the-art methods,
analyzing their unique features, effectiveness, and limitations. Finally, we
propose potential directions for future research, including the integration of
multi-modal data, multi-criteria rating information, and ethical
considerations.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要