{"title":"软对比序列推荐","authors":"Yabin Zhang, Zhenlei Wang, Wenhui Yu, Lantao Hu, Peng Jiang, Kun Gai, Xu Chen","doi":"10.1145/3665325","DOIUrl":null,"url":null,"abstract":"Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this paper. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.","PeriodicalId":5,"journal":{"name":"ACS Applied Materials & Interfaces","volume":"53 27","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Soft Contrastive Sequential Recommendation\",\"authors\":\"Yabin Zhang, Zhenlei Wang, Wenhui Yu, Lantao Hu, Peng Jiang, Kun Gai, Xu Chen\",\"doi\":\"10.1145/3665325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this paper. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.\",\"PeriodicalId\":5,\"journal\":{\"name\":\"ACS Applied Materials & Interfaces\",\"volume\":\"53 27\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Materials & Interfaces\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3665325\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Materials & Interfaces","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3665325","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this paper. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.