Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval最新文献

Exploiting Stopping Time to Evaluate Accumulated Relevance 利用停止时间评估累积相关性

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409832

M. Ferrante, N. Ferro

引用次数: 3

Approximate Nearest Neighbor Search and Lightweight Dense Vector Reranking in Multi-Stage Retrieval Architectures 多阶段检索体系中的近似最近邻搜索和轻量级密集向量重排序

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409818

Zhengkai Tu, Wei Yang, Zihang Fu, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, Jimmy J. Lin

引用次数: 8

Personalising and Diversifying the Listening Experience 个性化和多样化的聆听体验

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3410464

M. Lalmas

引用次数: 2

Modeling Search Engine Performance Measurement 建模搜索引擎性能测量

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3410466

M. Sanderson

引用次数: 1

Query Performance Prediction for Multifield Document Retrieval 多字段文档检索的查询性能预测

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409821

Haggai Roitman, Y. Mass, Guy Feigenblat, Roee Shraga

{"title":"Query Performance Prediction for Multifield Document Retrieval","authors":"Haggai Roitman, Y. Mass, Guy Feigenblat, Roee Shraga","doi":"10.1145/3409256.3409821","DOIUrl":"https://doi.org/10.1145/3409256.3409821","url":null,"abstract":"The goal of the query performance prediction (QPP) task is to estimate retrieval effectiveness in the absence of relevance judgments. We consider a novel task of predicting the performance of multifield document retrieval. In this setting, documents are assumed to consist of several different textual descriptions (fields) on which the query is being evaluated. Overall, we study three predictor types. The first type applies a given basic QPP method directly on the retrieval's outcome. Building on the idea of reference-lists, the second type utilizes several pseudo-effective (PE) reference-lists. Each such list is retrieved by further evaluating the query over a specific (single) document field. The third predictor is built on the assumption that, a high agreement among the single-field PE reference-lists attests to a more effective retrieval. Using three different multifield document retrieval tasks we demonstrate the merits of our extended QPP methods. Specifically, we show the important role that the intrinsic agreement among the single-field PE reference-lists plays in this extended QPP task.","PeriodicalId":430907,"journal":{"name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131231620","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: 2

A Multistage Ranking Strategy for Personalized Hotel Recommendation with Human Mobility Data 基于人类移动数据的个性化酒店推荐的多阶段排名策略

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409810

Yiwei Li, M. Fan, Jizhou Huang, Kan Li

引用次数: 1

Towards Memorable Information Retrieval 面向记忆信息检索

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409830

S. Qiu, U. Gadiraju, A. Bozzon

{"title":"Towards Memorable Information Retrieval","authors":"S. Qiu, U. Gadiraju, A. Bozzon","doi":"10.1145/3409256.3409830","DOIUrl":"https://doi.org/10.1145/3409256.3409830","url":null,"abstract":"Information overload is a problem many of us can relate to nowadays. The deluge of user generated content on the Internet, and the easy accessibility to a vast amount of data compounds the problem of remembering and retaining information that is consumed. To make information consumed more memorable, strategies such as note-taking have been found to be effective by augmenting human memory under specific conditions. This is based on the rationale that humans tend to recall information better if they have produced the information themselves. Previous works in online education have shown that conversational systems can improve learning effects. Although memorization is an important part of learning, the effect of conversation on human memorability remains unexplored. We aim to address this knowledge gap through an experimental study, by investigating human memorability in a classical information retrieval setup. We explore the impact of note-taking affordances and conversational interfaces on the memorability of information consumed by users. Our results show that traditional web search and note-taking have positive effects on knowledge gain, while the search engine with a conversational interface has the potential to augment long-term memorability. This work highlights the benefits of using note-taking and conversational interfaces to aid human memorability. Our findings have important implications on building information retrieval systems that cater to optimizing memorability of information consumed.","PeriodicalId":430907,"journal":{"name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777765","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: 13

Learning to Rank Entities for Set Expansion from Unstructured Data 学习从非结构化数据中进行集扩展的实体排序

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409811

Puxuan Yu, Razieh Rahimi, Zhiqi Huang, J. Allan

引用次数: 6

A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation 个性化Top-n推荐的混合条件变分自编码器模型

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409835

Yaxiong Wu, C. Macdonald, I. Ounis

{"title":"A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation","authors":"Yaxiong Wu, C. Macdonald, I. Ounis","doi":"10.1145/3409256.3409835","DOIUrl":"https://doi.org/10.1145/3409256.3409835","url":null,"abstract":"The interactions of users with a recommendation system are in general sparse, leading to the well-known cold-start problem. Side information, such as age, occupation, genre and category, have been widely used to learn latent representations for users and items in order to address the sparsity of users' interactions. Conditional Variational Autoencoders (CVAEs) have recently been adapted for integrating side information as conditions to constrain the learned latent factors and to thereby generate personalised recommendations. However, the learning of effective latent representations that encapsulate both user (e.g. demographic information) and item side information (e.g. item categories) is still challenging. In this paper, we propose a new recommendation model, called Hybrid Conditional Variational Autoencoder (HCVAE) model, for personalised top-n recommendation, which effectively integrates both user and item side information to tackle the cold-start problem. Two CVAE-based methods -- using conditions on the learned latent factors, or conditions on the encoders and decoders -- are compared for integrating side information as conditions. Our HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions. Thorough and extensive experiments conducted on both the MovieLens and Ta-feng datasets demonstrate that the HCVAE model conditioned on user category preferences with conditions on the learned latent factors can significantly outperform common existing top-n recommendation approaches such as MF-based and VAE/CVAE-based models.","PeriodicalId":430907,"journal":{"name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","volume":"1048 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122485481","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: 12

ICTIR Tutorial: Modern Query Performance Prediction: Theory and Practice ICTIR教程:现代查询性能预测:理论与实践

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-09-14 DOI: 10.1145/3409256.3409813

Haggai Roitman

引用次数: 7