Profile Diversity for Query Processing using User Recommendations
作者：
Highlights：
• We propose a specific scoring function for content and profile diversification using a probabilistic model.
• We propose a greedy threshold-based top-k algorithm to process queries using our profile diversity score using the concept of candidate list.
• We propose various techniques for optimizing the computation of top-k diversified profiles.
• To evaluate the benefits of our scoring function and optimization techniques, we ran our algorithms using three datasets: two from Del.icio.us and one from Flickr. The results show that our approach increases the overall quality of recommendations and that our optimizing strategies reduce significantly the response time of the diversified top-k computation
摘要
Highlights•We propose a specific scoring function for content and profile diversification using a probabilistic model.•We propose a greedy threshold-based top-k algorithm to process queries using our profile diversity score using the concept of candidate list.•We propose various techniques for optimizing the computation of top-k diversified profiles.•To evaluate the benefits of our scoring function and optimization techniques, we ran our algorithms using three datasets: two from Del.icio.us and one from Flickr. The results show that our approach increases the overall quality of recommendations and that our optimizing strategies reduce significantly the response time of the diversified top-k computation
论文关键词：Search and recommendation,Diversity,Top-k
论文评审过程：Received 4 July 2014, Revised 2 September 2014, Accepted 4 September 2014, Available online 16 September 2014.
论文官网地址：https://doi.org/10.1016/j.is.2014.09.001
