An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations
An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations
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一种可调整的重新排名方法，用于提高产品推荐的个体和总体多样性
DOI：
10.1007/s10660-018-09325-4
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发表时间：
2018-12
影响因子：
3.9
通讯作者：
Deng Weiwei
中科院分区：
文献类型：
--
作者：
Wang Qian;Yu Jijun;Deng Weiwei
产品推荐的有效性之前是根据推荐准确性进行评估的。近年来，产品推荐的个体多样性和聚合多样性被认为是评价推荐有效性的重要维度。然而，任一分集的增益通常是以精度为代价的，并且一个分集的增加并不能保证另一个分集的显著改善。已经进行了一些尝试来实现推荐准确性和个体多样性之间或推荐准确性和聚合多样性之间的合理权衡。很少有人注意在产品建议的三个重要方面之间取得平衡。为了解决这个问题，我们提出了一个可调整的重新排名的方法，采用了两个新的排名标准，以改善这两个diversity。生成三个排名列表以分别保证推荐准确性、个体多样性和聚合多样性。三个排名列表最后用可调参数合并以生成推荐列表。为了评估所提出的方法，实验进行从阿里巴巴获得的数据集。结果表明，该方法实现了更高的改进，在这两个差异比基线方法时，牺牲相同数量的推荐精度。
The effectiveness of product recommendations is previously assessed based on recommendation accuracy. Recently, individual diversity and aggregate diversity of product recommendations have been recognized as important dimensions in evaluating the recommendation effectiveness. However, the gain of either diversity is usually at the cost of accuracy and the increase of one diversity does not guarantee a significant improvement in the other. A few attempts have been made to achieve reasonable trade-offs either between recommendation accuracy and individual diversity or between recommendation accuracy and aggregate diversity. Little attention has been paid to obtain a balance among the three important aspects of product recommendations. To address this problem, we propose an adjustable re-ranking approach that incorporates two new ranking criteria for improving both diversities. Three ranking lists are generated to guarantee recommendation accuracy, individual diversity, and aggregate diversity, respectively. The three ranking lists are finally merged with tunable parameters to generate a recommendation list. To evaluate the proposed method, experiments are conducted on a data set obtained from Alibaba. The results show that the proposed method achieves much higher improvements in both diversities than the baseline methods when sacrificing the same amount of recommendation accuracy.
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DOI：
10.1016/j.knosys.2017.03.023
发表时间：
2017-06
期刊：
Knowl. Based Syst.
影响因子：
--
作者：
Anupriya Gogna;A. Majumdar
通讯作者：
Anupriya Gogna;A. Majumdar
影响因子：
8.5
作者：
Lika, Blerina;Kolomvatsos, Kostas;Hadjiefthymiades, Stathes
通讯作者：
Hadjiefthymiades, Stathes
DOI：
--
发表时间：
2017-09
期刊：
ArXiv
影响因子：
--
作者：
Laming Chen;Guoxin Zhang;Hanning Zhou
通讯作者：
Laming Chen;Guoxin Zhang;Hanning Zhou
DOI：
10.1109/hicss.2016.91
发表时间：
2016-01
期刊：
2016 49th Hawaii International Conference on System Sciences (HICSS)
影响因子：
--
作者：
Jinkun Wang;Yezheng Liu;Jianshan Sun;Yuanchun Jiang;Chunhua Sun
通讯作者：
Jinkun Wang;Yezheng Liu;Jianshan Sun;Yuanchun Jiang;Chunhua Sun
DOI：
10.1016/j.dss.2017.10.002
发表时间：
2017-12
期刊：
Decis. Support Syst.
影响因子：
--
作者：
Qian Zhang;Dianshuang Wu;Jie Lu;Feng Liu;Guangquan Zhang
通讯作者：
Qian Zhang;Dianshuang Wu;Jie Lu;Feng Liu;Guangquan Zhang