Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He
{"title":"上下文感知推荐的多分支卷积网络","authors":"Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He","doi":"10.1145/3397271.3401218","DOIUrl":null,"url":null,"abstract":"Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-Branch Convolutional Network for Context-Aware Recommendation\",\"authors\":\"Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He\",\"doi\":\"10.1145/3397271.3401218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.\",\"PeriodicalId\":252050,\"journal\":{\"name\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3397271.3401218\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397271.3401218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Branch Convolutional Network for Context-Aware Recommendation
Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.