Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval最新文献

Incorporating Hierarchical Domain Information to Disambiguate Very Short Queries 结合层次域信息消除极短查询歧义

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344251

Hamed Bonab, Mohammad Aliannejadi, John Foley, J. Allan

引用次数: 3

Deep Learning of Human Information Foraging Behavior with a Search Engine 基于搜索引擎的人类信息觅食行为深度学习

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344231

Xi Niu, Xiangyu Fan

{"title":"Deep Learning of Human Information Foraging Behavior with a Search Engine","authors":"Xi Niu, Xiangyu Fan","doi":"10.1145/3341981.3344231","DOIUrl":"https://doi.org/10.1145/3341981.3344231","url":null,"abstract":"In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126853292","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: 4

MAISoN 2019: The 3rd International Workshop on Mining Actionable Insights from Social Networks MAISoN 2019:第三届从社交网络中挖掘可操作见解国际研讨会

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3350529

Marcelo G. Armentano, E. Bagheri, Julia Kiseleva, Frank W. Takes

引用次数: 0

A Factored Relevance Model for Contextual Point-of-Interest Recommendation

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344230

Anirban Chakraborty, Debasis Ganguly, A. Caputo, S. Lawless

{"title":"A Factored Relevance Model for Contextual Point-of-Interest Recommendation","authors":"Anirban Chakraborty, Debasis Ganguly, A. Caputo, S. Lawless","doi":"10.1145/3341981.3344230","DOIUrl":"https://doi.org/10.1145/3341981.3344230","url":null,"abstract":"The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines.","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840365","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: 6

Learning a Better Negative Sampling Policy with Deep Neural Networks for Search 用深度神经网络学习一种更好的搜索负抽样策略

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344220

Daniel Cohen, Scott M. Jordan, W. Bruce Croft

引用次数: 14

Utilizing Passages in Fusion-based Document Retrieval 基于融合的文献检索中段落的利用

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344212

Haggai Roitman, Y. Mass

引用次数: 2

A Study of Query Performance Prediction for Answer Quality Determination 面向回答质量判定的查询性能预测研究

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344219

Haggai Roitman, Shai Erera, Guy Feigenblat

引用次数: 14

Listwise Neural Ranking Models 列表神经排序模型

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344245

Razieh Rahimi, Ali Montazeralghaem, J. Allan

引用次数: 8

Generative Adversarial Networks in Precision Oncology 精准肿瘤学中的生成对抗网络

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3344238

Leandro von Werra, Marcel Schöngens, E. Uzun, Carsten Eickhoff

引用次数: 4

Statistical Significance Testing in Theory and in Practice 理论与实践中的统计显著性检验

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2019-09-23 DOI: 10.1145/3341981.3358959

Ben Carterette

{"title":"Statistical Significance Testing in Theory and in Practice","authors":"Ben Carterette","doi":"10.1145/3341981.3358959","DOIUrl":"https://doi.org/10.1145/3341981.3358959","url":null,"abstract":"The past 25 years have seen a great improvement in the rigor of experimentation on information access problems. This is due primarily to three factors: high-quality, public, portable test collections such as those produced by TREC (the Text REtreval Conference~citetrecbook ), the increased ease of online A/B testing on large user populations, and the increased practice of statistical hypothesis testing to determine whether observed improvements can be ascribed to something other than random chance. Together these create a very useful standard for reviewers, program committees, and journal editors; work on information access (IA) problems such as search and recommendation increasingly cannot be published unless it has been evaluated offline using a well-constructed test collection or online on a large user base and shown to produce a statistically significant improvement over a good baseline. But, as the saying goes, any tool sharp enough to be useful is also sharp enough to be dangerous. Statistical tests of significance are widely misunderstood. Most researchers and developers treat them as a \"black box'': evaluation results go in and a p-value comes out. But because significance is such an important factor in determining what directions to explore and what is published or deployed, using p-values obtained without thought can have consequences for everyone working in IA. Ioannidis has argued that the main consequence in the biomedical sciences is that most published research findings are false; could that be the case for IA as well?","PeriodicalId":173154,"journal":{"name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114442169","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: 1