Improving Diversity Of User-Based Two-Step Recommendation Algorithm With Popularity Normalization
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2016(2016)
摘要
Recommender systems become increasingly significant in solving the information overload problem. Beyond conventional rating prediction and ranking prediction recommendation technologies, two-step recommendation algorithms have been demonstrated that they have outstanding accuracy performance in top-N recommendation tasks. However, their recommendation lists are biased towards popular items. In this paper, we propose a popularity normalization method to improve the diversity of user-based two-step recommendation algorithms. Experiment results show that our proposed approach improves the diversity performance significantly while maintaining the advantage of two-step recommendation approaches on accuracy metrics.
更多查看译文
关键词
Recommender system,Collaborative filtering,Diversity,Two-step recommendation,Popularity normalization
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要