Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval最新文献

Session details: Session 2B - Conversational Search 会话详细信息:会话2B -会话搜索

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3484641

Hamed Zamani

引用次数: 0

Session details: Session 1C - Content Analysis 会议详情:会议1C -内容分析

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3484639

Makoto P. Kato

引用次数: 0

Fairness and Control of Exposure in Two-sided Markets 双边市场中的公平与风险敞口控制

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472226

T. Joachims

{"title":"Fairness and Control of Exposure in Two-sided Markets","authors":"T. Joachims","doi":"10.1145/3471158.3472226","DOIUrl":"https://doi.org/10.1145/3471158.3472226","url":null,"abstract":"A large number of two-sided markets are now mediated by search and recommender systems, ranging from online retail and streaming entertainment to employment and romantic-partner matching. I will discuss in this talk how the design decisions that go into these search and recommender systems carry substantial power in shaping markets and allocating opportunity to the participants. This does not only raise legal and fairness questions, but also questions about how these systems shape incentives and the long-term effectiveness of the market. At the core of these questions lies the problem of where to rank each item, and how this affects both sides of the market. While it is well understood how to maximize the utility to the users, this talk focuses on how rankings affect the items that are being ranked. From the items perspective, the ranking system is an arbiter of exposure and thus economic opportunity. I will discuss how machine learning algorithms that follow the conventional Probability Ranking Principle [1] can lead to undesirable and unfair exposure allocation for both exogenous and endogenous reasons. Exogenous reasons often manifest themselves as biases in the training data, which then get reflected in the learned ranking policy. But even when trained with unbiased data, reasons endogenous to the system can lead to unfair or undesirable allocation of opportunity. To overcome these challenges, I will present new machine learning algorithms [2,3,4] that directly address both endogenous and exogenous factors, allowing the designer to tailor the ranking policy to be appropriate for the specific two-sided market.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633558","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: 3

Effective and Privacy-preserving Federated Online Learning to Rank 有效和隐私保护联邦在线学习排名

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472236

Shuyi Wang, Bing Liu, Shengyao Zhuang, G. Zuccon

引用次数: 8

Semantic Hilbert Space for Interactive Image Retrieval 交互式图像检索的语义希尔伯特空间

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472253

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz

{"title":"Semantic Hilbert Space for Interactive Image Retrieval","authors":"Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz","doi":"10.1145/3471158.3472253","DOIUrl":"https://doi.org/10.1145/3471158.3472253","url":null,"abstract":"The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different system states and their dynamic changes thereof based on observations (such as queries, relevance judgements). We will be able to learn the input multimodal representation and relationships between textual-image queries for retrieving images. Our experiments are conducted on the MIT States and Fashion200k datasets that demonstrate the effectiveness of finding particular images autocratically when the user inputs are semantically expressive.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130187418","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

A Novel Approach for Mining Team Leaders in Community Question Answering 采矿团队领导在社区问答中的新方法

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472230

Sepideh Maleki, Mahmood Neshati

引用次数: 0

The Impact of Entity Cards on Learning-Oriented Search Tasks 实体卡对学习型搜索任务的影响

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472255

Sara Salimzadeh, David Maxwell, C. Hauff

{"title":"The Impact of Entity Cards on Learning-Oriented Search Tasks","authors":"Sara Salimzadeh, David Maxwell, C. Hauff","doi":"10.1145/3471158.3472255","DOIUrl":"https://doi.org/10.1145/3471158.3472255","url":null,"abstract":"Entity cards are a common occurrence in today's web Search Engine Results Pages (SERPs). SERPs provide information on a complex information object in a structured manner. Typically, they combine data from several search verticals. They have been shown to: (i) increase users' engagement with the SERP; and (ii) improve decision making for certain types of searches (such as health searches). In this paper, we investigate whether the benefits of showing entity cards also extend to the Search as Learning (SAL) domain. Do learners learn more when entity cards are present on the SERP? To answer this question, we designed a series of learning-oriented search tasks (with a minimum search time of 15 minutes), and conducted a crowdsourced Interactive Information Retrieval (IIR) user study (N=144) with four interface conditions: (i) a control with no entity cards; (ii) displaying relevant entity cards; (iii) displaying somewhat relevant entity cards; and (iv) displaying non-relevant entity cards. Our results show that (i) entity cards do not have an effect on participants' learning, but (ii) they do significantly impact participants' search behaviours across a range of dimensions (such as the dwell time and search session duration).","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122030705","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

Untangling the Concept of Task in Information Seeking and Retrieval 信息查找与检索中任务概念的厘清

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472259

Ayah Soufan, I. Ruthven, L. Azzopardi

引用次数: 1

A Fast and Exact Randomisation Test for Comparing Two Systems with Paired Data 用配对数据比较两个系统的快速和精确的随机化检验

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3471158.3472228

Rikiya Suzuki, T. Sakai

引用次数: 0

Session details: Session 2A - Ranking (2) 会议详情:会议2A -排名(2)

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-07-11 DOI: 10.1145/3484640

D. Hiemstra

引用次数: 0