A Matrix Factorization Based Recommendation Algorithm for Science and Technology Resource Exploitation

The past decade witnesses a great progress of personalized recommendation systems, which extract potential characteristics of users and items based on users' ratings information on items, and then recommend other items that may be of interest to users. However, traditional recommendation algorithms ignore the users social relationships and suffer with the cold start problem. In this paper, we propose a recommendation algorithm based on similarity calculation matrix factorization (SCMF) with the consideration of users social relationships and items associated relationships when ratings information is available. At the same time, when ratings information is unavailable, the proposed algorithm can obtain potential feature relationships and makes personalized recommendations. We also introduce a penalty factor of positive items to the similarity calculation equation in case of over fitting. By this way, the SCFM algorithm can improve the prediction accuracy and alleviate the problem of cold start of sparse data. We compared our approach with other 4 algorithms and conducted experiments on 4 common used data sets. Regarding the experimental results, the recommendation precision of SCMF can be improved by 2% to 9% compared with the traditional best algorithm, and by 0.17% to 18% for cold start users. Additionally, our approaches were applied to patent data resource exploitation, provided by Wanfang patent retrieval system. The experimental results also verify the effectiveness of SCMF algorithm in improving recommendation performance.