Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第7页

Understanding Temporal Query Intent 理解时态查询意图

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767792

Mohammed Hasanuzzaman, S. Saha, G. Dias, S. Ferrari

引用次数: 6

Practical Lessons for Gathering Quality Labels at Scale 大规模收集质量标签的实践教训

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2776778

Omar Alonso

引用次数: 12

Linse: A Distributional Semantics Entity Search Engine Linse:分布式语义实体搜索引擎

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767871

J. Sales, A. Freitas, S. Handschuh, Brian Davis

引用次数: 2

Sign-Aware Periodicity Metrics of User Engagement for Online Search Quality Evaluation 基于符号感知的用户参与周期度量用于在线搜索质量评估

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767814

Alexey Drutsa

引用次数: 10

Automatic Feature Generation on Heterogeneous Graph for Music Recommendation 基于异构图的音乐推荐特征自动生成

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767808

Chun Guo, Xiaozhong Liu

{"title":"Automatic Feature Generation on Heterogeneous Graph for Music Recommendation","authors":"Chun Guo, Xiaozhong Liu","doi":"10.1145/2766462.2767808","DOIUrl":"https://doi.org/10.1145/2766462.2767808","url":null,"abstract":"Online music streaming services (MSS) experienced exponential growth over the past decade. The giant MSS providers not only built massive music collection with metadata, they also accumulated large amount of heterogeneous data generated from users, e.g. listening history, comment, bookmark, and user generated playlist. While various kinds of user data can potentially be used to enhance the music recommendation performance, most existing studies only focused on audio content features and collaborative filtering approaches based on simple user listening history or music rating. In this paper, we propose a novel approach to solve the music recommendation problem by means of heterogeneous graph mining. Meta-path based features are automatically generated from a content-rich heterogeneous graph schema with 6 types of nodes and 16 types of relations. Meanwhile, we use learning-to-rank approach to integrate different features for music recommendation. Experiment results show that the automatically generated graphical features significantly (p<0.0001) enhance state-of-the-art collaborative filtering algorithm.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434673","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

How many results per page?: A Study of SERP Size, Search Behavior and User Experience 每页有多少个结果?: SERP大小、搜索行为和用户体验的研究

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767732

D. Kelly, L. Azzopardi

{"title":"How many results per page?: A Study of SERP Size, Search Behavior and User Experience","authors":"D. Kelly, L. Azzopardi","doi":"10.1145/2766462.2767732","DOIUrl":"https://doi.org/10.1145/2766462.2767732","url":null,"abstract":"The provision of \"ten blue links\" has emerged as the standard for the design of search engine result pages (SERPs). While numerous aspects of SERPs have been examined, little attention has been paid to the number of results displayed per page. This paper investigates the relationships among the number of results shown on a SERP, search behavior and user experience. We performed a laboratory experiment with 36 subjects, who were randomly assigned to use one of three search interfaces that varied according to the number of results per SERP (three, six or ten). We found subjects' click distributions differed significantly depending on SERP size. We also found those who interacted with three results per page viewed significantly more SERPs per query; interestingly, the number of SERPs they viewed per query corresponded to about 10 search results. Subjects who interacted with ten results per page viewed and saved significantly more documents. They also reported the greatest difficulty finding relevant documents, rated their skills the lowest and reported greater workload, even though these differences were not significant. This work shows that behavior changes with SERP size, such that more time is spent focused on earlier results when SERP size decreases.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130747075","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: 66

Towards Quantifying the Impact of Non-Uniform Information Access in Collaborative Information Retrieval 对协同信息检索中非统一信息访问影响的量化研究

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767779

N. Htun, Martin Halvey, L. Baillie

引用次数: 4

Features of Disagreement Between Retrieval Effectiveness Measures 检索有效性度量差异的特征

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767824

Timothy Jones, Paul Thomas, Falk Scholer, M. Sanderson

引用次数: 6

A Head-Weighted Gap-Sensitive Correlation Coefficient 头部加权的间隙敏感相关系数

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767793

Ning Gao, Douglas W. Oard

引用次数: 6

Untangling Result List Refinement and Ranking Quality: a Framework for Evaluation and Prediction 解缠结果列表细化和质量排序:一个评估和预测的框架

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767740

Jiyin He, M. Bron, A. D. Vries, L. Azzopardi, M. de Rijke

{"title":"Untangling Result List Refinement and Ranking Quality: a Framework for Evaluation and Prediction","authors":"Jiyin He, M. Bron, A. D. Vries, L. Azzopardi, M. de Rijke","doi":"10.1145/2766462.2767740","DOIUrl":"https://doi.org/10.1145/2766462.2767740","url":null,"abstract":"Traditional batch evaluation metrics assume that user interaction with search results is limited to scanning down a ranked list. However, modern search interfaces come with additional elements supporting result list refinement (RLR) through facets and filters, making user search behavior increasingly dynamic. We develop an evaluation framework that takes a step beyond the interaction assumption of traditional evaluation metrics and allows for batch evaluation of systems with and without RLR elements. In our framework we model user interaction as switching between different sublists. This provides a measure of user effort based on the joint effect of user interaction with RLR elements and result quality. We validate our framework by conducting a user study and comparing model predictions with real user performance. Our model predictions show significant positive correlation with real user effort. Further, in contrast to traditional evaluation metrics, the predictions using our framework, of when users stand to benefit from RLR elements, reflect findings from our user study. Finally, we use the framework to investigate under what conditions systems with and without RLR elements are likely to be effective. We simulate varying conditions concerning ranking quality, users, task and interface properties demonstrating a cost-effective way to study whole system performance.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132957416","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