Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

MVL: Multi-View Learning for News Recommendation MVL:新闻推荐的多视角学习

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401294

Santosh T.Y.S.S, Avirup Saha, Niloy Ganguly

引用次数: 21

Sentiment-guided Sequential Recommendation 情绪导向的顺序推荐

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401330

Lin Zheng, Naicheng Guo, Weihao Chen, Jin Yu, Dazhi Jiang

引用次数: 20

Learning Discriminative Joint Embeddings for Efficient Face and Voice Association 人脸与语音高效关联的学习判别联合嵌入

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401302

Rui Wang, Xin Liu, Y. Cheung, Kai Cheng, Nannan Wang, Wentao Fan

{"title":"Learning Discriminative Joint Embeddings for Efficient Face and Voice Association","authors":"Rui Wang, Xin Liu, Y. Cheung, Kai Cheng, Nannan Wang, Wentao Fan","doi":"10.1145/3397271.3401302","DOIUrl":"https://doi.org/10.1145/3397271.3401302","url":null,"abstract":"Many cognitive researches have shown the natural possibility of face-voice association, and such potential association has attracted much attention in biometric cross-modal retrieval domain. Nevertheless, the existing methods often fail to explicitly learn the common embeddings for challenging face-voice association tasks. In this paper, we present to learn discriminative joint embedding for face-voice association, which can seamlessly train the face subnetwork and voice subnetwork to learn their high-level semantic features, while correlating them to be compared directly and efficiently. Within the proposed approach, we introduce bi-directional ranking constraint, identity constraint and center constraint to learn the joint face-voice embedding, and adopt bi-directional training strategy to train the deep correlated face-voice model. Meanwhile, an online hard negative mining technique is utilized to discriminatively construct hard triplets in a mini-batch manner, featuring on speeding up the learning process. Accordingly, the proposed approach is adaptive to benefit various face-voice association tasks, including cross-modal verification, 1:2 matching, 1:N matching, and retrieval scenarios. Extensive experiments have shown its improved performances in comparison with the state-of-the-art ones.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125023137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Domain Adaptation with Reconstruction for Disaster Tweet Classification 基于重构的领域自适应灾害推文分类

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401242

Xukun Li, Doina Caragea

引用次数: 11

Fair Classification with Counterfactual Learning 基于反事实学习的公平分类

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401291

M. Tavakol

引用次数: 6

An Intent-guided Collaborative Machine for Session-based Recommendation 基于会话推荐的意图引导协同机器

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401273

Zhiqiang Pan, Fei Cai, Yanxiang Ling, M. de Rijke

{"title":"An Intent-guided Collaborative Machine for Session-based Recommendation","authors":"Zhiqiang Pan, Fei Cai, Yanxiang Ling, M. de Rijke","doi":"10.1145/3397271.3401273","DOIUrl":"https://doi.org/10.1145/3397271.3401273","url":null,"abstract":"Session-based recommendation produces item predictions mainly based on anonymous sessions. Previous studies have leveraged collaborative information from neighbor sessions to boost the recommendation accuracy for a given ongoing session. Previous work often selects the most recent sessions as candidate neighbors, thereby failing to identify the most related neighbors to obtain an effective neighbor representation. In addition, few existing methods simultaneously consider the sequential signal and the most recent interest in an ongoing session. In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR). ICM-SR encodes an ongoing session by leveraging the prior sequential items and the last item to generate an accurate session representation, which is then used to produce initial item predictions as intent. After that, we design an intent-guided neighbor detector to locate the correct neighbor sessions. Finally, the representations of the current session and the neighbor sessions are adaptively combined by a gated fusion layer to produce the final item recommendations. Experiments conducted on two public benchmark datasets show that ICM-SR achieves a significant improvement in terms of Recall and MRR over the state-of-the-art baselines.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126929450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search EARS 2020:第三届可解释推荐和搜索国际研讨会

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401468

Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, C. Shah

引用次数: 2

Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation 基于块级搜索推荐优化的混合目标指向解码器训练

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401236

Harsh Kohli

引用次数: 1

Extractive Snippet Generation for Arguments 为参数生成提取代码段

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401186

Milad Alshomary, Nick Düsterhus, Henning Wachsmuth

引用次数: 20

Searching the Web for Cross-lingual Parallel Data 搜索跨语言并行数据的网络

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401417

Ahmed El-Kishky, Philipp Koehn, Holger Schwenk

{"title":"Searching the Web for Cross-lingual Parallel Data","authors":"Ahmed El-Kishky, Philipp Koehn, Holger Schwenk","doi":"10.1145/3397271.3401417","DOIUrl":"https://doi.org/10.1145/3397271.3401417","url":null,"abstract":"While the World Wide Web provides a large amount of text in many languages, cross-lingual parallel data is more difficult to obtain. Despite its scarcity, this parallel cross-lingual data plays a crucial role in a variety of tasks in natural language processing with applications in machine translation, cross-lingual information retrieval, and document classification, as well as learning cross-lingual representations. Here, we describe the end-to-end process of searching the web for parallel cross-lingual texts. We motivate obtaining parallel text as a retrieval problem whereby the goal is to retrieve cross-lingual parallel text from a large, multilingual web-crawled corpus. We introduce techniques for searching for cross-lingual parallel data based on language, content, and other metadata. We motivate and introduce multilingual sentence embeddings as a core tool and demonstrate techniques and models that leverage them for identifying parallel documents and sentences as well as techniques for retrieving and filtering this data. We describe several large-scale datasets curated using these techniques and show how training on sentences extracted from parallel or comparable documents mined from the Web can improve machine translation models and facilitate cross-lingual NLP.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5