关键词: 推荐算法, 数据稀疏, 冷启动, 分布式表示技术

Abstract:

Traditional recommender algorithms have obtained great performance and have been widely used in practice, meanwhile it faces some serious problems of data sparsity and cold start. With the growth of the data around the world, and the data structures of information are increasingly richer, how to correctly capture the feature of user preferences from these data have become the key issue. The distributed representation methods provide a new solution for the issue above. By reviewing the recommender algorithms based on distributed presentation technology in recent years, this paper analyzes the differences and advantages between these algorithms and traditional one, and summarizes the practical scene of these algorithms. Finally, the prospects for future development of recommender algorithm based on distributed representation is discussed, in order to provide references for subsequent related work.

Key words: recommender algorithm, data sparsity, cold start, distributed representation technology