Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems
作者：
Highlights：
• Provides a hybrid methodology for alleviating the cold start.
• Evaluates some of the most well-known probability metrics for scoring rules.
• Develops a novel scoring function for ranking rules.
• Exploits semantic web advantages (e.g. reusability, interoperability, etc.)
• A qualitative rule set which can be used by other services is created.
摘要
•Provides a hybrid methodology for alleviating the cold start.•Evaluates some of the most well-known probability metrics for scoring rules.•Develops a novel scoring function for ranking rules.•Exploits semantic web advantages (e.g. reusability, interoperability, etc.)•A qualitative rule set which can be used by other services is created.
论文关键词：Context-aware recommender systems,Rule-based systems,Location-Based Services,Association rules,Cold start problem
论文评审过程：Received 2 August 2017, Revised 25 January 2018, Accepted 26 January 2018, Available online 31 January 2018, Version of Record 15 February 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.044
