In Twitter-like social networking services, the "@'' symbol can be used with the tweet to mention users whom the user wants to alert regarding the message. An automatic suggestion to the user of a small list of candidate names can improve communication efficiency. Previous work usually used several most recent tweets or randomly select historical tweets to make an inference about this preferred list of names. However, because there are too many historical tweets by users and a wide variety of content types, the use of several tweets cannot guarantee the desired results. In this work, we propose the use of a novel cooperative multi-agent approach to mention recommendation, which incorporates dozens of more historical tweets than earlier approaches. The proposed method can effectively select a small set of historical tweets and cooperatively extract relevant indicator tweets from both the user and mentioned users. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.
{"title":"Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning","authors":"Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou, Xuanjing Huang","doi":"10.1145/3331184.3331237","DOIUrl":"https://doi.org/10.1145/3331184.3331237","url":null,"abstract":"In Twitter-like social networking services, the \"@'' symbol can be used with the tweet to mention users whom the user wants to alert regarding the message. An automatic suggestion to the user of a small list of candidate names can improve communication efficiency. Previous work usually used several most recent tweets or randomly select historical tweets to make an inference about this preferred list of names. However, because there are too many historical tweets by users and a wide variety of content types, the use of several tweets cannot guarantee the desired results. In this work, we propose the use of a novel cooperative multi-agent approach to mention recommendation, which incorporates dozens of more historical tweets than earlier approaches. The proposed method can effectively select a small set of historical tweets and cooperatively extract relevant indicator tweets from both the user and mentioned users. Experimental results demonstrate that the proposed method outperforms 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":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77783667","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}
Alexey Drutsa, Gleb Gusev, E. Kharitonov, Denis Kulemyakin, P. Serdyukov, I. Yashkov
We present you a program of a balanced mix between an overview of academic achievements in the field of online evaluation and a portion of unique industrial practical experience shared by both the leading researchers and engineers from global Internet companies. First, we give basic knowledge from mathematical statistics. This is followed by foundations of main evaluation methods such as A/B testing, interleaving, and observational studies. Then, we share rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics (emphasizing best practices and common pitfalls). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently. We invite software engineers, designers, analysts, and managers of web services and software products, as well as beginners, advanced specialists, and researchers to learn how to make web service development effectively data-driven.
{"title":"Effective Online Evaluation for Web Search","authors":"Alexey Drutsa, Gleb Gusev, E. Kharitonov, Denis Kulemyakin, P. Serdyukov, I. Yashkov","doi":"10.1145/3331184.3331378","DOIUrl":"https://doi.org/10.1145/3331184.3331378","url":null,"abstract":"We present you a program of a balanced mix between an overview of academic achievements in the field of online evaluation and a portion of unique industrial practical experience shared by both the leading researchers and engineers from global Internet companies. First, we give basic knowledge from mathematical statistics. This is followed by foundations of main evaluation methods such as A/B testing, interleaving, and observational studies. Then, we share rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics (emphasizing best practices and common pitfalls). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently. We invite software engineers, designers, analysts, and managers of web services and software products, as well as beginners, advanced specialists, and researchers to learn how to make web service development effectively data-driven.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76048875","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}
This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question "Which SERP is more relevant?'' and the other based on a diversity question "Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.
{"title":"Which Diversity Evaluation Measures Are \"Good\"?","authors":"T. Sakai, Zhaohao Zeng","doi":"10.1145/3331184.3331215","DOIUrl":"https://doi.org/10.1145/3331184.3331215","url":null,"abstract":"This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question \"Which SERP is more relevant?'' and the other based on a diversity question \"Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85783501","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}
This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
{"title":"Web Table Extraction, Retrieval and Augmentation","authors":"Shuo Zhang, K. Balog","doi":"10.1145/3331184.3331385","DOIUrl":"https://doi.org/10.1145/3331184.3331385","url":null,"abstract":"This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88185123","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}
Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort "by price" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.
{"title":"Fast Approximate Filtering of Search Results Sorted by Attribute","authors":"F. M. Nardini, Roberto Trani, Rossano Venturini","doi":"10.1145/3331184.3331227","DOIUrl":"https://doi.org/10.1145/3331184.3331227","url":null,"abstract":"Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort \"by price\" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)\"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88444377","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}
In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.
{"title":"Learning from Fact-checkers: Analysis and Generation of Fact-checking Language","authors":"Nguyen Vo, Kyumin Lee","doi":"10.1145/3331184.3331248","DOIUrl":"https://doi.org/10.1145/3331184.3331248","url":null,"abstract":"In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88057025","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}
Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma
Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.
{"title":"Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation","authors":"Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331205","DOIUrl":"https://doi.org/10.1145/3331184.3331205","url":null,"abstract":"Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88639624","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}
Sérgio D. Canuto, Thiago Salles, Thierson Couto, Marcos André Gonçalves
We propose new solutions that enhance and extend the already very successful application of meta-features to text classification. Our newly proposed meta-features are capable of: (1) improving the correlation of small pieces of evidence shared by neighbors with labeled categories by means of synthetic document representations and (local and global) hyperplane distances; and (2) estimating the level of error introduced by these newly proposed and the existing meta-features in the literature, specially for hard-to-classify regions of the feature space. Our experiments with large and representative number of datasets show that our new solutions produce the best results in all tested scenarios, achieving gains of up to 12% over the strongest meta-feature proposal of the literature.
{"title":"Similarity-Based Synthetic Document Representations for Meta-Feature Generation in Text Classification","authors":"Sérgio D. Canuto, Thiago Salles, Thierson Couto, Marcos André Gonçalves","doi":"10.1145/3331184.3331239","DOIUrl":"https://doi.org/10.1145/3331184.3331239","url":null,"abstract":"We propose new solutions that enhance and extend the already very successful application of meta-features to text classification. Our newly proposed meta-features are capable of: (1) improving the correlation of small pieces of evidence shared by neighbors with labeled categories by means of synthetic document representations and (local and global) hyperplane distances; and (2) estimating the level of error introduced by these newly proposed and the existing meta-features in the literature, specially for hard-to-classify regions of the feature space. Our experiments with large and representative number of datasets show that our new solutions produce the best results in all tested scenarios, achieving gains of up to 12% over the strongest meta-feature proposal of the literature.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75651549","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}
{"title":"Session details: Session 1B: Health and Social Media","authors":"Mark D. Smucker","doi":"10.1145/3349676","DOIUrl":"https://doi.org/10.1145/3349676","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90301308","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}
Cen Chen, Chilin Fu, Xujun Hu, Xiaolu Zhang, Jun Zhou, Xiaolong Li, F. S. Bao
A customer service bot is now a necessary component of an e-commerce platform. As a core module of the customer service bot, user intent prediction can help predict user questions before they ask. A typical solution is to find top candidate questions that a user will be interested in. Such solution ignores the inter-relationship between questions and often aims to maximize the immediate reward such as clicks, which may not be ideal in practice. Hence, we propose to view the problem as a sequential decision making process to better capture the long-term effects of each recommendation in the list. Intuitively, we formulate the problem as a Markov decision process and consider using reinforcement learning for the problem. With this approach, questions presented to users are both relevant and diverse. Experiments on offline real-world dataset and online system demonstrate the effectiveness of our proposed approach.
{"title":"Reinforcement Learning for User Intent Prediction in Customer Service Bots","authors":"Cen Chen, Chilin Fu, Xujun Hu, Xiaolu Zhang, Jun Zhou, Xiaolong Li, F. S. Bao","doi":"10.1145/3331184.3331370","DOIUrl":"https://doi.org/10.1145/3331184.3331370","url":null,"abstract":"A customer service bot is now a necessary component of an e-commerce platform. As a core module of the customer service bot, user intent prediction can help predict user questions before they ask. A typical solution is to find top candidate questions that a user will be interested in. Such solution ignores the inter-relationship between questions and often aims to maximize the immediate reward such as clicks, which may not be ideal in practice. Hence, we propose to view the problem as a sequential decision making process to better capture the long-term effects of each recommendation in the list. Intuitively, we formulate the problem as a Markov decision process and consider using reinforcement learning for the problem. With this approach, questions presented to users are both relevant and diverse. Experiments on offline real-world dataset and online system demonstrate the effectiveness of our proposed approach.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81381496","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：