Recommendation Strategy using Expanded Neighbor Collaborative Filtering

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• 英文论文题名：Recommendation Strategy using Expanded Neighbor Collaborative Filtering
• 论文作者：Bin Wang ; Qi Gao ; Xiao Xue Feng ; Feng Pan
• 英文论文作者：Bin Wang ; Qi Gao ; Xiao Xue Feng ; Feng Pan ; School of Automation ; Beijing Institute of Technology ; Kunming-BIT Industry Technology Research Institute INC
• 年：2017
• 作者机构：School of Automation,Beijing Institute of Technology;Kunming-BIT Industry Technology Research Institute INC;
• 英文论文关键词：Recommender System ; Collaborative Filtering ; Second-Order Neighbor ; Coverage ; Diversity
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（A）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
• 语种：英文
• 分类号：TP391.3
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：5
• 文件大小：441k
• 原文格式：D
• 会议级别：全国

摘要

The evaluation of recommender system is often biased towards accuracy, which is hard to balance all participants' interests. In this paper, a novel recommendation strategy using expanded neighbor collaborative filtering(ECF) is presented.Different from the standard collaborative filtering(CF), this recommendation strategy takes into account the second-order neighbors, which are expected to contribute to the coverage and diversity of recommendation. A transferring similarity is proposed to link the given user with second-order neighbors via nearest neighbors. Based on M ovie Lens dataset, the strategy was test on several typical similarity indexes. The numerical results confirmed the improvements on coverage and diversity compared to the benchmark CF, without affecting accuracy obviously.
The evaluation of recommender system is often biased towards accuracy, which is hard to balance all participants' interests. In this paper, a novel recommendation strategy using expanded neighbor collaborative filtering(ECF) is presented.Different from the standard collaborative filtering(CF), this recommendation strategy takes into account the second-order neighbors, which are expected to contribute to the coverage and diversity of recommendation. A transferring similarity is proposed to link the given user with second-order neighbors via nearest neighbors. Based on M ovie Lens dataset, the strategy was test on several typical similarity indexes. The numerical results confirmed the improvements on coverage and diversity compared to the benchmark CF, without affecting accuracy obviously.

引文

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