利用回溯式和前瞻式转换器使顺序推荐多样化

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chaoyu Shi, Pengjie Ren, Dongjie Fu, Xin Xin, Shansong Yang, Fei Cai, Zhaochun Ren, Zhumin Chen
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引用次数: 0

摘要

以往关于序列推荐(SR)的研究主要集中在优化推荐准确性上。然而,在增强推荐多样性方面仍存在巨大差距,尤其是在短交互序列方面。短序列中交互信息的有限性阻碍了推荐者全面模拟用户意图的能力,从而影响了推荐的多样性和准确性。有鉴于此,我们提出了用于逆向均衡推荐的前瞻性和后瞻性转换器(TRIER)。TRIER 首先通过回溯学习预测用户潜在的历史互动,从而引入额外信息并扩展短互动序列,然后从多个增强序列中捕捉用户的潜在意图,从而解决短互动序列信息不足的问题。最后,TRIER 通过学习尽可能多的潜在意图来生成多样化的推荐列表。为了评估 TRIER 的有效性,我们在三个基准数据集上进行了广泛的实验。实验结果表明,TRIER 的性能明显优于最先进的方法,在 Steam 数据集上的列表内距离(ILD@5)多样性提高了 11.36%,在 Yelp 数据集上的列表内距离(ILD@5)提高了 3.43%,在 Beauty 数据集上的类别覆盖率(CC@5)提高了 3.77%。至于准确性,在 Yelp 数据集上,我们观察到 HR@5 和 NDCG@5 分别显著提高了 7.62% 和 8.63%。此外,我们还发现 TRIER 对短交互序列的准确性和多样性有更显著的改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diversifying Sequential Recommendation with Retrospective and Prospective Transformers

Previous studies on sequential recommendation (SR) have predominantly concentrated on optimizing recommendation accuracy. However, there remains a significant gap in enhancing recommendation diversity, particularly for short interaction sequences. The limited availability of interaction information in short sequences hampers the recommender’s ability to comprehensively model users’ intents, consequently affecting both the diversity and accuracy of recommendation. In light of the above challenge, we propose reTrospective and pRospective Transformers for dIversified sEquential Recommendation (TRIER). The TRIER addresses the issue of insufficient information in short interaction sequences by first retrospectively learning to predict users’ potential historical interactions, thereby introducing additional information and expanding short interaction sequences, and then capturing users’ potential intents from multiple augmented sequences. Finally, the TRIER learns to generate diverse recommendation lists by covering as many potential intents as possible.

To evaluate the effectiveness of TRIER, we conduct extensive experiments on three benchmark datasets. The experimental results demonstrate that TRIER significantly outperforms state-of-the-art methods, exhibiting diversity improvement of up to 11.36% in terms of intra-list distance (ILD@5) on the Steam dataset, 3.43% ILD@5 on the Yelp dataset and 3.77% in terms of category coverage (CC@5) on the Beauty dataset. As for accuracy, on the Yelp dataset, we observe notable improvement of 7.62% and 8.63% in HR@5 and NDCG@5, respectively. Moreover, we found that TRIER reveals more significant accuracy and diversity improvement for short interaction sequences.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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