From Bipartite Network to Function: Online-Nearline-Offline Hybrid Music Recommendation

Abstract

In order to solve problems of stubborn cold start, data sparseness and shortage of each single recommendation algorithm in the music recommendation, Online-Nearline-Offline hybrid MRS is used in this paper. By mixing collaborative filtering and PersonalRank algorithm according to their different characteristics, ONO hybrid model can rationally make up for the shortage reciprocally. Moreover, specific rules for cold start and K-Means for data sparseness are also put forward. For illustration, an entirely new user is utilized to go through the recommendation process both in single algorithm model and ONO hybrid model. The results show that the measured MAE reflecting the satisfaction is the most smooth and the lowest under the condition of hybrid MRS. The ONO hybrid MRS is superior in solving cold star problem, taking the capacity of data in each period into accounts and making up for the single algorithm shortages. ISCC2017