Affiliation:
1. Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Abstract
In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system.
Funder
National Natural Science Foundation of China
Subject
General Engineering,General Mathematics
Reference31 articles.
1. Some Simple Economics of Crowdfunding
2. Successful crowdfunding: the effects of founder and project factors;M. H. Por
3. Application of dimensionality reduction in recommender system-a case study;B. M. Sarwar
4. Deep learning
5. Probabilistic group recommendation model for crowdfunding domains;R. Vineeth
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