Review-Based Cross-Domain Collaborative Filtering: A Neural Framework
Review-Based Cross-Domain Collaborative Filtering: A Neural Framework
复制标题
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Thanh-Nam Doan;Shaghayegh Sherry Sahebi
中科院分区：
文献类型：
--
作者：
Thanh-Nam Doan;Shaghayegh Sherry Sahebi
跨领域协同过滤推荐利用来自其他领域的数据（例如，电影评分）来预测用户在不同目标领域的兴趣（例如，推荐音乐）。目前大多数的跨领域推荐都专注于用户评分的建模，但对用户评论的关注有限。此外，由于这些推荐系统的复杂性，它们不能向用户提供任何信息来支持用户的决策。为了解决这些挑战，我们提出了深度混合跨域（DHCD）模型，这是一种跨域神经框架，可以同时预测用户评分，并提供有用的信息来加强跨域建议和支持用户决策。具体来说，DHCD通过联合建模用户产品评估的两个关键方面：评级和评论来增强预测评级。为了支持决策，它根据用户兴趣和项目特征跨领域建模并提供自然的类似评论的句子。该模型在集成来自两个以上领域的用户评价和评论信息方面具有鲁棒性。我们广泛的实验表明，DHCD在评级预测和评审生成任务中可以显著优于高级基线。对于评级预测任务，它优于跨域和单域协同过滤以及混合推荐系统。此外，我们的评论生成实验表明，在DHCD中提高了困惑评分和评论信息的传递。
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 crossdomain 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.