A Personalized Recommendation Algorithm with Time Factors for Technical Patent Matching
Wen Lei Bai, Jun Guo, Bao Ying Liu, Daguang Gan · 2020
How to accurately locate the information required by researchers in the massive patent resources is an urgent problem to be solved in the patent database. Traditional collaborative filtering algorithms, mainly based on the users' rating scores, ignore their interests or direction changes and the active user's effect on recommended precision. To solve these problems, this paper reconstructs the recommendation weight with the consideration of time factor and item similarity, and proposed a practical method for calculating the similarity of item. This method can obtain a more reliable item similarity and better-recommended results by weakening the influence of active users on similarity calculation. The experimental results show that the proposed method obviously improves the recommendation performance. Compared with several traditional recommendation algorithms and deep learning algorithms in five datasets, the precision and recall rate improve by 5.2% and 9.5% on average, respectively.