标题： 显式反馈与隐式反馈：推荐系统的一种综合方法 显示英文标题

标题： Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System

摘要： 推荐系统通过分析用户对不同品牌商品的潜在兴趣，更准确地推荐商品。 结合用户的评分相似性，用户隐式反馈的存在，如点击商品、查看商品规格、观看视频等，已被证明有助于学习用户的嵌入表示，这有助于更好地预测用户的评分。 大多数现有的推荐系统专注于评分和隐式反馈的建模，而忽略了用户的显式反馈。 显式反馈可以用来验证特定用户的可靠性，并可以用来了解用户的特点。 用户的特点意味着他们是哪种类型的评论者。 在本文中，我们探索了三种不同的推荐模型，以更高的准确性关注用户的显式反馈和隐式反馈。 第一个是RHC-PMF，它基于用户三个显式反馈（评分、有用性分数和中心性）更准确地预测用户的评分，第二个是RV-PMF，其中考虑了用户的隐式反馈（查看关系）。 最后一个模型是RHCV-PMF，其中考虑了两种类型的反馈。 在这个模型中，用户的显式反馈相似性表明他们可靠性和特点的相似性，而隐式反馈的相似性表明他们的偏好相似性。 在真实世界数据集上的大量实验，即Amazon.com在线评论数据集显示，我们的模型在用户评分预测方面比基线模型表现更好。 RHCV-PMF模型在冷启动用户和冷启动商品的评分预测方面也比基线模型表现更好。 显示英文摘要

摘要： Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users' embedding, that helps better rating prediction of users. Most existing recommender systems focus on modeling of ratings and implicit feedbacks ignoring users' explicit feedbacks. Explicit feedbacks can be used to validate the reliability of the particular users and can be used to learn about the users' characteristic. Users' characteristic mean what type of reviewers they are. In this paper, we explore three different models for recommendation with more accuracy focusing on users' explicit feedbacks and implicit feedbacks. First one is RHC-PMF that predicts users' rating more accurately based on user's three explicit feedbacks (rating, helpfulness score and centrality) and second one is RV-PMF, where user's implicit feedback (view relationship) is considered. Last one is RHCV-PMF, where both type of feedbacks are considered. In this model users' explicit feedbacks' similarity indicate the similarity of their reliability and characteristic and implicit feedback's similarity indicates their preference similarity. Extensive experiments on real world dataset, i.e. Amazon.com online review dataset shows that our models perform better compare to base-line models in term of users' rating prediction. RHCV-PMF model also performs better rating prediction compare to baseline models for cold start users and cold start items.