Privacy-Preserving IR 2016

Information retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies and connections between these two disciplines. However, there have been very limited efforts to connect the two important disciplines. On the other hand, due to lack of mature techniques in privacy-preserving IR, concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, tweets, sessions, and medical record retrieval. This privacy-preserving IR workshop aims to spur research that brings together the research fields of IR and privacy/security, and research that mitigates privacy threats in information retrieval by constructing novel algorithms and tools that enable web users to better understand associated privacy risks.

Query logs are valuable resources for Information Retrieval (IR) research. However, because they are also rich in private and personal information, the huge concern of leaking user privacy prevents query logs from being shared from the search companies to the broad research community. Bothered by the lack of good research data for years, the authors of this paper are motivated to explore ways to generate anonymized query logs that can still be effectively used to support the search task. We introduce a framework to anonymize query logs by differential privacy, the latest development in privacy research. The framework is empirically evaluated against multiple search algorithms on their retrieval utility, measured in standard IR evaluation metrics, using the anonymized logs. The experiments show that our framework is able to achieve a good balance between retrieval utility and privacy.

Privacy in Social Media

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Purpose Prior Information Retrieval (IR) research synthesizes progress from individual studies, yet academia-industry collaboration dynamics remain unexplored. This study investigates: (1) productivity patterns and venues, (2) citations-downloads relationships, (3) topic evolution, and (4) collaboration trends. Design/methodology/approach We perform an analysis of 53,471 ACM IR papers (2000–2018) using bibliometrics and DistilBERT topic modeling. Findings We find that industry-involved papers preferred WWW/CIKM venues; collaborations dominated RecSys/CSCW. We see that academia-industry collaborations achieved the highest download-to-citation conversion rates. Academia focused on algorithms; industry on applications; collaborations bridged both with rising human-centered themes. Research implications This is a pioneering large-scale bibliometrics revealing collaboration’s impact on IR knowledge evolution and provides a methodological framework for cross-sector analysis. Practical implications The paper identifies optimal venues (RecSys/CSCW) for partnerships and guides joint initiatives (shared datasets, grants) to bridge academia-industry divides and enhance research translation. Originality/value This is the first large-scale bibliometric analysis of IR academia-industry collaboration. The paper finds many novel insights, including the fact that collaboration boosts citation efficiency, enables complementary specialization, and drives topic convergence.

This paper examines the contrast between United States and European Union laws concerning social media users’ right to remove their online presence permanently. Currently, the United States and European Union represent two distinct approaches to the right of individuals to permanently remove personal content from social media. U.S. law favors social media companies keeping profile content within the digital sphere even when that person no longer wants it there. The European Union’s approach social media privacy gives users more rights to remove themselves entirely from social media permanently (General Data Protection Regulation, Article 17, 2012). Using Myres McDougal’s (1959) legal theory of international laws’ effect on national policy, this legal study examines the social media privacy laws of the United States and European Union concerning user control of personal content. From this analysis, future implications of this international conflict, specifically the legal delineation of public and private spheres in the 21st Century, are suggested.

In 2012, Sogou, a major Chinese web search engine released a large-scale query log containing 43.5M user interactions, including submitted queries and clicked web page search results. This query log offers a deep sample of queries over a two day period from 30th December 2011 to 1st January 2012. In August 2013, we identified 1.4M predominantly Chinese language unique search result URLs that were clicked at least three times in this query log. We crawled the HTML content of these URLs to construct the supplementary SOGOU-2012-CRAWL dataset, which we release in this work. A real large-scale query log with accompanying crawl such as this offers several opportunities for reproducible information retrieval (IR) research, including query classification, intent modelling and indexing strategy. In this paper we first detail the query log and crawl dataset construction and characteristics. Following this, to demonstrate potential applications we use the crawl to indicatively analyse various time-based patterns in web content and search behaviour. In particular, we study the distribution of language-independent date expressions in the crawled web content. Based on this, we propose a simple approach for modelling the past/present/future temporal intent of queries based on the date the query was submitted by the user, and the dates appearing in the clicked search results. We observe several prominent temporal patterns which may lead to novel time-aware IR approaches.

Accurate ordering or ranking over instances is of paramount importance for several applications (Faria et al. Learning to rank for content-based image retrieval. In: Proceedings of the Multimedia Information Retrieval Conference, pp. 285–294, 2010; Veloso et al. Learning to rank at query-time using association rules. In: Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), pp. 267–274, 2008; Veloso et al. J Inf Data Manag 1(3): 567–582, 2010; Veloso and Meira, Efficient on-demand opinion mining. In: Proceedings of the Brazilian Symposium on Databases (SBBD), pp. 332–346, 2007; Veloso et al. Automatic moderation of comments in a large on-line journalistic environment. In: Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 234–237, AAAI, 2007).One clear application is Information Retrieval, where documents retrieved by search engines must be ranked according to the corresponding relevance to the query (Trotman, Inf Ret 8(3):359–381, 2005).Many features may affect the relevance of such documents, and, thus, it is difficult to adapt ranking functions manually. Recently, a body of empirical evidence has emerged suggesting that methods that automatically learn ranking functions offer substantial improvements in enough situations to be regarded as a relevant advance for applications that depend on ranking . Hence, learning ranking functions has attracted significant interest from the machine learning community. In the context of Information Retrieval, the conventional approach to this learning task is to assume the availability of examples (i.e., a training data, \({S},\) which typically consists of document features and the corresponding relevance to specific queries), from which a learning function can be learned. When a new query is given, the documents associated with this query are ranked according to the learned function (i.e., this function gives a score to a document indicating its relevance with regard to the query). In this chapter we present ranking algorithms based on demand-driven associative classification .

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

The article describes the project on music information retrieval that has been carried out at the University of Padova, Italy. The research work has been characterized by the synergy of the modular integration of sound techniques of melody processing and of statistical information retrieval. After illustrating the background from which the project has originated, we describe the complete process, from methodology design through evaluation and system implementation. Conclusions, impacts on research in music information retrieval, and future directions are also described.

Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

This paper researched two issues; targeted advertising and politics in social media. In its findings, it was apparent that many social media platforms actively collected user data and used big data to store and analyze it so as to profile the users. From these profiles, targeted adverts were served to the users on their social media feeds. The data collection process was however found to be unethical and full of privacy intrusions. Concerning politics, it was found out that social media had been used as a political tool. There were findings that most users were annoyed with political posts being shown on their feeds. The culprits were found to be the algorithms behind social media feeds and suggestions and the users themselves. The paper gave recommendations said to be temporary solutions for the issues surrounding target advertising and politics in social media.

Information retrieval (IR) systems aim to select relevant material from large collections of information in response to user queries. The approaches used to accomplish this have been the focus of much research and development over the past 50 years, and have led to the algorithms underlying many commercial and Web-based search engines today. This entry describes the common components that go into the design of IR systems (from text processing to inverted file indexes). The major classes (or models) of retrieval algorithms (Boolean, vector, and probabilistic) are described along with formal definitions of the basic form of these algorithms and some of the variations in common use in IR research. In addition, the entry examines query expansion techniques, and in particular relevance feedback, and how they are used in IR systems

In this paper, the concept of security and privacy in social media, or social networking will be discussed. First, a brief history and the concept of social networking will be introduced. Many of the security risks associated with using social media are presented. Also, the issue of privacy and how it relates to security are described. Based on these discussions, some solutions to improve a user’s privacy and security on social networks will be suggested. Our research will help the readers to understand the security and privacy issues for the social network users, and some steps which can be taken by both users and social network organizations to help improve security and privacy.

INTRODUCTION:Increasing numbers of research papers about information retrieval for Health Technology Assessments (HTA), systematic reviews and other evidence syntheses are being published. It is time-consuming for information specialists to keep up-to-date with the latest developments in the field. To help searchers with this challenge, the Interest Group on Information Retrieval (IRG) of Health Technology Assessment International (HTAi) has compiled the best available research evidence on information retrieval aspects into an open-access web resource: Summarized Research in Information Retrieval for HTA (SuRe Info). The resource can be accessed at http://www.sure-info.orgMETHODS:The Sure Info team run topic-specific search strategies in selected relevant databases to identify information retrieval methods publications that fulfil the SuRe Info inclusion criteria. Eligible publications receive a structured abstract containing a brief critical appraisal. Key messages for search practice based on the appraisals and accepted best practice are summarized into topic-specific chapters.RESULTS:SuRe Info currently offers fourteen chapters, with more in development. SuRe Info chapters fall into two categories: (i) chapters about general search methods that are used across all types of research, such as how to develop search strategies and the availability and use of search filters, and (ii) chapters summarizing the methods to use when searching for specific aspects of HTA (as defined in the European Network for HTA (EUnetHTA) HTA Core Model®), including searching for evidence on clinical effectiveness and safety, and identifying economic evaluations. References at the end of each chapter are linked to appraisals of publications that have been used to develop each chapter. Links to the full-text of the publications are provided when freely available. The SuRe Info chapters are reviewed every six months and updated if new evidence is identified or if resources change.CONCLUSIONS:SuRe Info is a unique resource, identifying and summarizing current best research evidence on information retrieval aspects for HTA. It supports the timely uptake of potential efficiencies arising from new evidence that may be incorporated into the evidence identification processes of HTA organizations.