A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users

In this paper, a new collaborative filtering method is proposed based on finding similar users directly and indirectly to overcome sparsity challenge. Moreover, selecting these users through extracting dominant opinion patterns leads to tackling scalability. In this method, frequent opinions between users are extracted to be used as dominant patterns. Then, users corresponding to the same dominant pattern are considered as direct similar users. Direct similar users who have seen more items and have corresponded to more than one pattern are regarded as reference users. Each reference user mediates between users who may have no/few commonly seen items and they are considered as indirect similar users. Utilizing indirect similar user helps the method to predict opinion of query user about items, which have not been seen by any direct similar user before. Clearly, indirect users are selected based on speculation using available information about direct users' preferences. Thus, the effect of indirect users is considered to be stricter than that of direct users on the final prediction step. Experiments conducted on MovieLens small, MovieLens 100 k, MovieLens 1 M, and Jester datasets showed that the proposed method outperforms the previously introduced methods, especially on sparse data. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

A new collaborative filtering method is proposed in this paper, which overcomes sparsity challenge by finding similar users directly and indirectly. The method selects users through extracting dominant opinion patterns and outperforms previously introduced methods, especially on sparse data.