Building user profiles based on sequences for content and collaborative filtering
作者：
Highlights：
• A generic method to transform users to sequences using collaborative and content-based information.
• We define a user similarity metric based on LCS algorithm (extensible to any string-based comparison algorithm) than can produce competitive recommendations.
• Definition of various parameters (confidence, preference, normalizations, and threshold) to produce better recommendations in the LCS-based algorithm.
• Normalization functions help to improve accuracy without hurting diversity or novelty.
• Preference filtering does not decrement the performance but reduces its computational cost.
摘要
•A generic method to transform users to sequences using collaborative and content-based information.•We define a user similarity metric based on LCS algorithm (extensible to any string-based comparison algorithm) than can produce competitive recommendations.•Definition of various parameters (confidence, preference, normalizations, and threshold) to produce better recommendations in the LCS-based algorithm.•Normalization functions help to improve accuracy without hurting diversity or novelty.•Preference filtering does not decrement the performance but reduces its computational cost.
论文关键词：Hybrid recommender systems,Preference filtering,Content-based filtering,Collaborative filtering,Longest Common Subsequence
论文评审过程：Received 6 April 2018, Revised 26 September 2018, Accepted 8 October 2018, Available online 18 October 2018, Version of Record 18 October 2018.
论文官网地址：https://doi.org/10.1016/j.ipm.2018.10.003
