The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods— I tem L evel Similarity M atrix F actorization (ILMF) and U ser L evel Similarity M atrix F actorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose I tem- U ser L evel Similarity M atrix F actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.

中文翻译：

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基于新颖相似性度量方法的时间方面-情感推荐模型

电子商务的爆炸式增长带动了推荐系统的发展。推荐系统旨在通过分析用户的消费记录，提供一组满足用户个性化需求的商品。然而，购买数据的及时性和反馈数据的隐性对现有的推荐方法提出了严峻的挑战。为了缓解这些挑战，我们从用户和物品的角度利用用户的消费记录，通过对物品和用户级别的数据进行建模，其中物品级别的值反映了物品的等级，用户级别的值反映了物品的等级。用户的购买意向。在本文中，我们从公共网站收集商品的描述信息和评论，然后采用情感分析技术对用户级别和商品级别的相似性进行建模，分别。特别是，我们扩展了传统的潜在因素模型并提出了两种新方法——一世项目大号相似度米atrixF演员化（ILMF）和üSER大号相似度米atrixF演员化（ULMF）——通过引入两种新的相似性度量方法。在 ILMF 和 ULMF 中，潜在因素和显性方面之间的一致性很自然地被纳入到学习用户和项目的潜在因素中，这样我们就可以更准确地预测用户对不同项目的偏好。此外，我们建议一世温度üSER大号相似度米atrixF演员化（IULMF），它结合了这两种方法来研究它们对最终性能的贡献。对真实数据集的实验评估表明，我们的方法在精度和 NDCG 方面都优于基线方法。