Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

中文翻译：

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结合用户反馈和协同过滤的新型时间感知混合推荐方案

如今，推荐系统已广泛应用于各个领域，以解决信息过载的问题。协作过滤和基于内容的模型是推荐系统中的代表性解决方案。但是，基于内容的模型存在一些缺点，例如单一的推荐结果以及对用户偏好的有效感知不足；而对于协作过滤模型，存在一个冷启动问题，并且这种模型受其影响很大。采用聚类算法。为了解决这些问题，本文提出了一种基于协同过滤和基于内容的混合推荐方案。在该方案中，我们提出了时间影响因子的概念，并在此基础上建立了时间感知用户偏好模型。也，利用用户对推荐项目的反馈来提高我们建议的推荐模型的准确性。最后，提出的混合模型结合了内容推荐和基于逻辑回归算法的协同过滤结果。