Abstract
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.
Highlights
With the rapid development of the Internet, users can enjoy rich information services and convenient social interaction through Internet applications [1]
The information overload problem in Internet applications is becoming more and more serious, which makes it difficult for users to choose what they really like. erefore, various recommendation models are widely used to help users locate information. These popular recommendation models can be divided into collaborative filtering, content-based, and hybrid approaches. e collaborative filtering method [2,3,4] is based on the view that the higher the similarity between users, the more the overlapping of user preferences. e content-based approach [5, 6] is based on representations to recommend items, and these representations are usually extracted from descriptions
(3) HRBRM [35]: the most important achievement of this study is to present a novel approach in hybrid recommendation systems, which identifies the user similarity neighborhoods from implicit information
Summary
With the rapid development of the Internet, users can enjoy rich information services and convenient social interaction through Internet applications [1]. Erefore, various recommendation models are widely used to help users locate information These popular recommendation models can be divided into collaborative filtering, content-based, and hybrid approaches. Users’ profiles cannot be accurately acquired because of a lack of sufficient user behavior information [2, 6] Another issue is that the content-based approach is slow to perceive the change of user preference. To mine more implicit information in hybrid models, in [15], a Bayesian network model combining content-based and collaborative filtering is proposed, and the Bayesian network is used to calculate the joint probability distribution of user access time and resource information to obtain the user’s interest of the provided resource. Is work is built on the prior work of content-based and collaborative filtering, and we consider the time factor and user feedback to enhance the performance of recommendation The feedback from users on recommendations and the timeliness of the recommendations have not been paid enough attention. is work is built on the prior work of content-based and collaborative filtering, and we consider the time factor and user feedback to enhance the performance of recommendation
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