Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

Abstract

This paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method.