The recommendation service of the shareholding for fund companies based on improved collaborative filtering method

Abstract

Through the effective processing of the financial dataset of listed companies and the improvement of the traditional collaborative filtering algorithm, this paper proposes a recommendation service which can predict the shareholding proportion of the listed companies more accurately in order to help fund companies to make reasonable decisions to invest listed companies. This service includes two important methods. To deal with the sparsity of data, one solution is to integrate similarity of user ratings and similarity of common-selected item to collaborative filtering algorithm. The relationship of common-selected item defined in this paper fully considers user’s common scored items. The greater the relationship value between users is, more similar two users are. The other solution is mainly applicable to new companies with very few indicators. The approach proposed in this paper is mainly based on the similarity between items, and generates different filling value for each unrated item based on re-filling with item-based collaborative filtering algorithm. From the perspective of the recommendation method itself, the method proposed in this paper is superior to the traditional standard collaborative filtering algorithm and the SVD algorithm.