Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第7页

Coarse-to-Fine Grained Classification 粗到细粒度分类

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331336

Yuqi Huo, Yao Lu, Yulei Niu, Zhiwu Lu, Ji-Rong Wen

引用次数: 9

Query-Task Mapping Query-Task映射

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331286

Michael Völske, Ehsan Fatehifar, Benno Stein, Matthias Hagen

引用次数: 7

A Systematic Comparison of Methods for Finding Good Premises for Claims 寻找良好索赔前提方法的系统比较

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331282

Lorik Dumani, Ralf Schenkel

引用次数: 15

Improving Collaborative Metric Learning with Efficient Negative Sampling 利用高效负抽样改进协同度量学习

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331337

Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam

引用次数: 16

PSGAN

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331218

Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, Ji-rong Wen

{"title":"PSGAN","authors":"Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, Ji-rong Wen","doi":"10.1145/3331184.3331218","DOIUrl":"https://doi.org/10.1145/3331184.3331218","url":null,"abstract":"Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84767256","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: 38

Hot Topic-Aware Retweet Prediction with Masked Self-attentive Model 基于屏蔽自关注模型的热话题感知转发预测

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331236

Renfeng Ma, Xiangkun Hu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang

{"title":"Hot Topic-Aware Retweet Prediction with Masked Self-attentive Model","authors":"Renfeng Ma, Xiangkun Hu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang","doi":"10.1145/3331184.3331236","DOIUrl":"https://doi.org/10.1145/3331184.3331236","url":null,"abstract":"Social media users create millions of microblog entries on various topics each day. Retweet behaviour play a crucial role in spreading topics on social media. Retweet prediction task has received considerable attention in recent years. The majority of existing retweet prediction methods are focus on modeling user preference by utilizing various information, such as user profiles, user post history, user following relationships, etc. Yet, the users exposures towards real-time posting from their followees contribute significantly to making retweet predictions, considering that the users may participate into the hot topics discussed by their followees rather than be limited to their previous interests. To make efficient use of hot topics, we propose a novel masked self-attentive model to perform the retweet prediction task by perceiving the hot topics discussed by the users' followees. We incorporate the posting histories of users with external memory and utilize a hierarchical attention mechanism to construct the users' interests. Hence, our model can be jointly hot-topic aware and user interests aware to make a final prediction. Experimental results on a dataset collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76013320","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: 23

Effective Online Evaluation for Web Search 网络搜索的有效在线评估

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331378

Alexey Drutsa, Gleb Gusev, E. Kharitonov, Denis Kulemyakin, P. Serdyukov, I. Yashkov

引用次数: 3

Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning 提到Twitter中使用协同多智能体强化学习的推荐

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331237

Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou, Xuanjing Huang

引用次数: 14

Unified Collaborative Filtering over Graph Embeddings 图嵌入的统一协同过滤

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331224

Pengfei Wang, H. Chen, Yadong Zhu, Huawei Shen, Yongfeng Zhang

{"title":"Unified Collaborative Filtering over Graph Embeddings","authors":"Pengfei Wang, H. Chen, Yadong Zhu, Huawei Shen, Yongfeng Zhang","doi":"10.1145/3331184.3331224","DOIUrl":"https://doi.org/10.1145/3331184.3331224","url":null,"abstract":"Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the most important approaches to recommender systems research, and various CF models have been designed and applied to different scenarios. However, a challenging task is how to select the most appropriate CF model for a specific recommendation task. In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem. Specifically, UGrec models user and item interactions within a graph network, and sequential recommendation path is designed as a basic unit to capture the correlations between users and items. Mathematically, we show that many representative recommendation approaches and their variants can be mapped as a recommendation path in the graph. In addition, by applying a carefully designed attention mechanism on the recommendation paths, UGrec can determine the significance of each sequential recommendation path so as to conduct automatic model selection. Compared with state-of-the-art methods, our method shows significant improvements for recommendation quality. This work also leads to a deeper understanding of the connection between graph embeddings and recommendation algorithms.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73352730","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: 23

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331239

Sérgio D. Canuto, Thiago Salles, Thierson Couto, Marcos André Gonçalves

引用次数: 15