Diversity optimization for recommendation using improved cover tree

Diversity optimization for recommendation using improved cover tree
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使用改进的覆盖树进行推荐的多样性优化

DOI:
10.1016/j.knosys.2017.07.004
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发表时间:
2017-11
影响因子:
8.8
通讯作者:
Dong Yongqiang
Dong Yongqiang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Gu Liang;Yang Peng;Dong Yongqiang

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近年来,多样性优化在推荐系统中发挥着越来越重要的作用,以提高用户满意度,引起了学术界的广泛关注。本文提出了一种基于时间敏感语义覆盖树(T2SCT)的多样性优化方法。具体来说,我们首先定义T2SCT及其构建算法。在T2SCT的基础上,详细介绍了多样化商品选择算法和两种补充算法,以获得完整的多样化商品列表。然后,通过理论分析证明了所提方法的正确性。总的来说,所提出的方法可以在准确度上做出很小的妥协的情况下提出不同的建议。由于T2SCT固有的优势,该方法收敛速度快,项目新颖性好。我们在一个真实的数据集上进行了大量的实验来验证我们的方法的性能。结果表明,该方法是有效和高效的,优于其他传统方法。
Recently, diversity optimization has played an increasingly important role in recommender systems to improve user satisfaction, and it has attracted more attention in the research community. In this paper, we propose a novel diversity-optimization method based on a time-sensitive semantic cover tree (T2SCT). Specifically, we first define T2SCT and its construction algorithm. Based on T2SCT, we present details of the diversified item-selection algorithm and two supplement algorithms to obtain a complete diversified item list. Then, we give a theoretical analysis to prove the correctness of the proposed method. In general, the proposed method can make diverse recommendations with very little compromise on accuracy. Moreover, the proposed method converges quickly and exhibits good item novelty, owing to the inherent superiority of T2SCT. We conduct extensive experiments on a real-world dataset to verify the performance of our method. Results illustrate that the method is effective and efficient, outperforming other conventional approaches.
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