An improved collaborative recommendation algorithm based on optimized user similarity

Abstract

There are lots of issues existing in traditional collaborative filtering recommendation, such as data scarcities, cold start, recommendation accuracy and timeliness. And how to improve the efficiency and quality of recommendation is a key problem in collaborative recommendation. In the traditional collaborative filtering algorithms, the rating scale of different users for all projects sometimes may be neglected while calculating the similarity. Some algorithms such as adjusted cosine similarity algorithm and the Pearson similarity algorithm are proposed to optimize this problem, but there still exists the problem that the single rating scale is different for the same project with different users. It may result in similar resultant vector results when the users have significant differences for the score vectors on a common set. The substantial presence of this kind of phenomena has a direct impact on the accuracy of user similarity calculation. Furthermore, it will affect the target user's predicted score accuracy. To solve the problem, an improved collaborative recommendation algorithm based on optimized user similarity is proposed. A balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users. Also, the most appropriate balance factor threshold can be obtained by experiments, a series of reasonable experiments to validate the effectiveness of the proposed algorithm based on the threshold. Experimental results show that the proposed improved collaborative filtering algorithm based on user similarity can significantly optimize the accuracy of user similarity and get better recommendation results.