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HCIR Tables of Contents: 07080910111213

Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval

Fullname:Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
Location:Cambridge, Massachusetts
Dates:2012-Oct-04 to 2012-Oct-05
Standard No:ISBN: 978-1-4503-1796-2; ACM DL: Table of Contents; hcibib: HCIR12
Links:Conference Website | Short Papers | HCIR Challenge and Reports
Summary:The HCIR Symposium (formerly known as the "HCIR Workshop") has run annually since 2007. The event brings together academic researchers and industrial practitioners working at the intersection of HCI and IR to develop more sophisticated models, tools, and evaluation metrics to support activities such as interactive information retrieval and exploratory search. It provides an opportunity for attendees to informally share ideas via posters, small group discussions, selected short talks, and full papers.
  1. Papers
  2. Plenary Papers
  3. Poster Papers
  4. Posters


Knowledge journey: a web search interface for young users BIBAFull-Text 1
  Tatiana Gossen; Marcus Nitsche; Andreas Nürnberger
This paper describes a new user interface for a web search engine whose main target group are young users. We explain the main challenges for this user interface and discuss design decisions we made. Our interface is audio supported, contains possibilities for both searching through text input and navigating using menu categories, has a guidance figure for emotional support and a result storage functionality to support cognitive recall. It is also colourful which is appreciated by most children. A comparative study with 28 young users was conducted where we compared our user interface with a classic text search user interface provided by most current web search engines. We evaluated what features of both interfaces children like or dislike to further improve the interface.
Leyline: provenance-based search using a graphical sketchpad BIBAFull-Text 2
  Soroush Ghorashi; Carlos Jensen
The most effective strategy for finding files is to carefully arrange them into folders. This strategy breaks down for teams, where organizational schemes often differ between team members. It also breaks down when information is copied and reused as it becomes harder to track versions. As storage continues to grow and costs decline, the incentives to carefully archive old versions of files diminish. It is therefore important to explore new and improved search tools. The most common approach is keyword search, though recalling effective keywords can be challenging, especially as repositories grow and information flows across projects. A less common alternative is to use provenance --information about the creation, use and sharing of documents and their context, including collaborators. This paper presents a limited user study showing that provenance data is useful and desirable in search, and that an interface based on a graphical sketchpad is not only feasible, but efficient.
Modeling user variance in time-biased gain BIBAFull-Text 3
  Mark D. Smucker; Charles L. A. Clarke
Cranfield-style information retrieval evaluation considers variance in user information needs by evaluating retrieval systems over a set of search topics. For each search topic, traditional metrics model all users searching ranked lists in exactly the same manner and thus have zero variance in their per-topic estimate of effectiveness. Metrics that fail to model user variance overestimate the effect size of differences between retrieval systems. The modeling of user variance is critical to understanding the impact of effectiveness differences on the actual user experience. If the variance of a difference is high, the effect on user experience will be low. Time-biased gain is an evaluation metric that models user interaction with ranked lists that are displayed using document surrogates. In this paper, we extend the stochastic simulation of time-biased gain to model the variation between users. We validate this new version of time-biased gain by showing that it produces distributions of gain that agree well with actual distributions produced by real users. With a per-topic variance in its effectiveness measure, time-biased gain allows for the measurement of the effect size of differences, which allows researchers to understand the extent to which predicted performance improvements matter to real users.
Assigning search tasks designed to elicit exploratory search behaviors BIBAFull-Text 4
  Barbara M. Wildemuth; Luanne Freund
The goal of this paper is to provide guidance to researchers investigating exploratory search behaviors and exploratory search systems. It focuses on the design of search tasks assigned in such studies. Based on a review of past studies, a set of task characteristics associated with exploratory search tasks are identified: exploratory search tasks focus on learning and investigative search goals; they are general (rather than specific), open-ended, and often target multiple items/documents; they involve uncertainty and are motivated by ill-defined or ill-structured problems; they are dynamic and evolve over time; they are multi-faceted and may be procedurally complex; and they are often accompanied by other information or cognitive behaviors, such as sensemaking. Recommendations are provided for the design of search task descriptions that will elicit exploratory search behaviors.

Plenary Papers

Encouraging Behavior: A Foray into Persuasive Computing BIBAPDF 5
  Elena Agapie; Gene Golovchinsky; Pernilla Qvarfordt
Whereas longer queries have been shown to produce better results for information seeking tasks, people tend to type short queries. We created an interface designed to encourage people to construct longer queries, and evaluated it via an exploratory Mechanical Turk experiment. Results suggest that our interface manipulation may be effective for eliciting longer queries, but the effect is compromised when instructions to create longer queries are given.
Pseudo-Collaboration as a Method to Perform Selective Algorithmic Mediation in Collaborative IR Systems BIBAKPDF 6
  Roberto González-Ibáñez; Chirag Shah; Ryen White
Traditional recommendation systems suggest results based on data collected from users' actions. Many of the newer information retrieval (IR) systems incorporate social search or collective search signals as an extension to standard term-based retrieval algorithms. Systems based on social or collaborative search methods, however, do not consider when, how, and to what extent such support could help or hurt their users' search performance. In this poster we propose a novel approach of selective algorithmic mediation capable of identifying when a user should be aided by a collaborator and to what extent such help could enhance search success. We demonstrate the applicability and benefits of our approach through simulations using a pseudo-collaboration method on the log data of individual users and pairs of users gathered during a laboratory study with 131 participants. The results show that our approach can improve the search performance of both individual searchers and others collaborating intentionally by identifying and recommending regions in search processes with best chance of improvements, thus increasing the likelihood that users find more useful information with less effort.
Keywords: Collaborative information retrieval; Search effectiveness
Web User Interaction Mining from Touch-Enabled Mobile Devices BIBAPDF 7
  Jeff Huang; Abdigani Diriye
Web services that thrive on mining user interaction data such as search engines can currently track clicks and mouse cursor activity on their Web pages. Cursor interaction mining has been shown to assist in user modeling and search result relevance, and is becoming another source of rich that data scientists and search engineers can tap into. Due to the growing popularity of touch-enabled mobile devices, search systems may turn to tracking touch interactions in place of cursor interactions. However, unlike cursor interactions, touch interactions are difficult to record reliably and their coordinates have not been shown to relate to regions of user interest. A better approach may be to track the viewport coordinates instead, which the user must manipulate to view the content on a mobile device. These recorded viewport coordinates can potentially reveal what regions of the page interest users and to what degree. Using this information, search system can then improve the design of their pages or use this information in click models or learning to rank systems. In this position paper, we discuss some of the challenges faced in mining interaction data for new modes of interaction, and future research directions in this field.
Standards Opportunities around Data-Bearing Web Pages BIBAPDF 8
  David Karger
The web dramatically simplified, and thus democratized, the authoring and sharing of information. Anyone whose organization was operating a web server could author content and make it available for all web users to access with a single click. Unlike the plain-text content of Usenet newsgroups, this content could be beautified by the user who managed format and layout and embedded images. Users could control not only their content but also how it was presented. The result was an explosion of end-user-authored content that became available to others throughout the world.
Designing for Consumer Search Behaviour BIBAKPDF 9
  Tony Russell-Rose; Stephann Makri
In order to design better search experiences, we need to understand the complexities of human information-seeking behaviour. In this paper, we propose a model of information behavior based on the needs of users of consumer-oriented websites and search applications. The model consists of a set of search modes that users employ to satisfy their information search and discovery goals. We present design suggestions for how each of these modes can be supported in existing interactive systems, focusing in particular on those that have been supported in interesting or novel ways.
Keywords: Site search, information seeking, user behaviour, search modes, information discovery, user experience design

Poster Papers

Developing a Typology of Online Q&A Models and Recommending the Right Model for Each Question Type BIBAKPDF 10
  Erik Choi; Vanessa Kitzie; Chirag Shah
Although online Q&A services have increased in popularity, the field lacks a comprehensive typology to classify different kinds of services into model types. This poster categorizes online Q&A services into four model types -- community-based, collaborative, expert-based, and social. Drawing such a distinction between online Q&A models provides an overview for how these different types of online Q&A models differ from each other and suggests implications for mitigating weaknesses and bolstering strengths of each model based on the types of questions that are addressed within each. To demonstrate differences among these models an appropriate service was selected for each of them. Then, 500 questions were collected and analyzed for each of these services to classify question types into four categories -- information-seeking, advice-seeking, opinion-seeking, and non-information seeking. The findings suggest that information-seeking questions appear to be more suitable in either a collaborative Q&A environment or an expert-based Q&A environment, while opinion-seeking questions are more common in community-based Q&A. Social Q&A, on the other hand, provides an active forum for either seeking personal advice or seeking non-information related to either self-expression or self-promotion.
Keywords: Online Q&A; Community-based Q&A; Collaborative Q&A; Expert-based Q&A; Social Q&A; Question type
Investigating Positive and Negative Affects in Collaborative Information Seeking: A Pilot Study Report BIBAKPDF 11
  Roberto González-Ibáñez; Chirag Shah
Emotions and other affective processes have long been considered a key component in people's life. Despite research conducted in several research domains, little is known about the role of emotions in the information seeking process of both individuals and teams. This poster presents preliminary results from a pilot study conducted in order to evaluate design decisions, experimental protocol, tasks, and the system that will be used in research aiming to investigate the implications of positive and negative affects in the information search process of both individuals and teams. The pilot study involved 12 subjects randomly assigned to five experimental conditions in which a common information search task was performed. In each session, facial expressions, eye tracking data, electrodermal activity, desktop activity, users' actions, and searches as well as communication logs were collected. In addition, as sessions were conducted, observations about subjects' behaviors, system problems, and research protocols were made. Results from this pilot study suggest effects of both positive and negative affects at the level of performance, communication, and perceived difficulty, among other aspects. In addition to these preliminary findings, the poster also contributes a unique methodology, involving a rigorous design, for conducting such user studies.
Keywords: Collaborative search, interactive search, affects
To Ask or Not to Ask, That is The Question: Investigating Methods and Motivations for Online Q&A BIBAKPDF 12
  Vanessa Kitzie; Erik Choi; Chirag Shah
The popularity of studies conducted on online Q&A services has grown over the past few years. Here, online Q&A services are defined as facilitating an asker-answerer(s) relationship via the Internet, either asynchronously or synchronously. Online Q&A services have traditionally been divided into two separate services -- social Q&A (SQA) and Virtual Reference (VR) and studied separately, with lack of integrating mixed methods (e.g. SQA tends to be analyzed quantitatively versus VR, which tends to be analyzed qualitatively) in regard to approach and comparing SQA and VR services to determine whether gaps in one may be used to improve the other. This poster attempts to ameliorate this shortcoming by investigating both of them together using an online survey of 120 users, with a specific focus on the motivations for use of both services to determine (1) who uses online Q&A services and (2) the relationship between use of these services and general web searching behavior.
Keywords: Online Q&A; Virtual referencing; Social Q&A; Information seeking behaviors; User motivations
Information Seeking Tasks: Why Do Searchers Feel Difficult? BIBAKPDF 13
  Jingjing Liu; Chang Suk Kim
In this paper, we propose a framework that highlights the reasons for information task difficulty and evaluate and refine it using a case study results. Implications of our framework on search system design are suggested.
Keywords: Task difficulty categories, task features, user knowledge, system interface, document features
A Novel Architecture for a Smart Information Retrieval System Based on Opinions Engineering BIBAKPDF 14
  Youssef Meguebli; Fabrice Popineau; Bich-Lien Doan
In this paper, we present a novel architecture for personalized information retrieval (IR) and a simple scenario that illustrate the contribution of this architecture compared to current personalized IR. We use an extension of a Dung argumentation framework in order to improve the precision of our personalized information retrieval. We use also social media and search history to define the user-profile.
Keywords: Information retrieval, opinions engineering, Dung argumentation framework, User-profile model, social media
Finding Literary Themes with Relevance Feedback BIBAKPDF 15
  Aditi Muralidharan; Marti A. Hearst
A common task in text analysis is find conceptually-linked passages of text such as examples of themes, imagery, and descriptions of events. In the past, researchers looking to find such passages have had to rely on searching for sets of keywords. However, themes, descriptions, and imagery may surface with many different phrasings, making retrieval based on keyword search difficult. We investigated the application of relevance feedback to this problem. First, we implemented a relevance feedback system for sentence-length text. Then, we evaluated the system's ability to support gathering examples of themes in the works of Shakespeare. Participants with at least undergraduate backgrounds in English language or literature used either our system (N = 11) or keyword search (N = 12) to retrieve examples of a theme chosen by a professional Shakespeare scholar. Their examples were judged on relevance by our expert collaborator. Our results suggest that relevance feedback is effective. On average, participants with relevance feedback gathered more sentences, and more relevant sentences, with fewer searches than participants with keyword search. However, a larger study is needed to establish statistical significance.
Keywords: Information retrieval; relevance feedback; text analysis
InFrame-Browsing: Enhancing Standard Web Search BIBAKPDF 16
  Marcus Nitsche; Andreas Nuernberger
While adhoc-search is well supported by current web search engines like Google, more complex information seeking processes like exploratory searches often lack in support for integrated browsing, easy change of perspective on the same information and a support for Personal Information Management (PIM). However, these aspects are crucial when working with retrieved results in order to put them into context. In this paper we present a concept that enhances classic web search by different user interface elements and personalization components in order to improve user experience (UX) while users conduct complex search tasks. The concept has been prototypically implemented as a rich internet application to demonstrate its advantages towards ordinary web search user interfaces and has been evaluated by conducting an expert design review.
Keywords: Web Search, Search User Interface, Information Retrieval
Trailblazer: Towards the Design of an Exploratory Search User Interface BIBAKPDF 17
  Marcus Nitsche; Andreas Nuernberger
When conceptualizing user interfaces (UIs) to support exploratory search, designers need to take into account various aspects. In contrast to ordinary information retrieval UIs, exploratory search user interfaces (XSIs) need to support users in a more complex and often long term use scenario. An XSI needs to provide a visually appealing overview over retrieved search results, it should offer simple ways to interact with the result set and offer easy ways of interaction to enhance the user's search experience by direct or indirect query refinement options. In this paper we identify the requirements of a specific XSI concept, describe this XSI concept and its features & present first results of a conducted usability study.
Keywords: Search User Interface, Exploratory Search, Search Trails
min: A Multi-Modal Web Interface for Math Search BIBAKPDF 18
  Christopher Sasarak; Kevin Hart; Siyu Zhu; Richard Pospesel; David Stalnaker; Lei Hu; Robert Livolsi; Richard Zanibbi
min is a web interface for constructing search queries that include mathematics, using the metaphor of an 'intelligent' blackboard. Formulas are entered using a combination of finger/mouse, keyboard, and images, with symbol recognition results shown using translucent overlays above the user's input. At the user's request, the blackboard is converted to LATEX and inserted in the query string, and the user's symbols are repositioned and resized on the blackboard to visualize the recognized layout of symbols on baselines (writing lines). Queries may include keywords and multiple LATEX expressions, and be submitted to a variety of search engines (e.g. Springer LATEX Search, Wolfram Alpha). min allows non-expert users to include math expressions in queries without special codes for mathematical symbols, providing text describing a formula, or requiring the use of a template-based equation editor.
Keywords: Mathematical Information Retrieval (MIR)
Search Tactics in Collaborative Exploratory Web Search BIBAKPDF 19
  Zhen Yue; Shuguang Han; Daqing He
This paper presents a user study to investigate search tactics involved in collaborative exploratory Web search. Both process and products related search tactics were examined through the analysis of transaction logs, chat logs and interview transcript. We found the search tactics employed by the participants vary on different search tasks.
Keywords: Collaborative web search, exploratory search, collaborative information behavior, search tactics
Developing a Dual-Process Information-Seeking Model for Exploratory Search BIBAKPDF 20
  Michael Zarro
In this work dual-process theory from the social psychology domain is introduced to help understand exploratory search behaviors and relate them to searchers' cognitive processes as they evaluate information resources. Our setting is the consumer health domain, where information seeking is often an exploratory search episode involving searchers who have high motivation but low ability to find and cognitively process information resources. Health consumers learn as they search, acquiring information from professional and peer-produced resources. Interactions between user, interface, and content will be aggregated to form a model of exploratory search.
Keywords: Exploratory search, health informatics, dual-process theory
Interactive Data Mining at the Speed of Thought BIBAKPDF 21
  Vladimir Zelevinsky
We describe a system that combines guided navigation with the computation of Pearson correlation coefficients to support the task of interactive data mining by creating dynamic previews of possible navigation states.
Keywords: Interactivity; faceted search; guided navigation; data mining; correlations
Do Users with Different Domain Knowledge Select Different Sets of Documents? BIBAKPDF 22
  Xiangmin Zhang; Jingjing Liu; Xiaojun Yuan; Michael J. Cole; Nick Belkin; Chang Liu
In this paper, we report findings on an examination of the document selecting behaviors of people with different levels of domain knowledge (DK). We found that people with high and low DK levels generally select different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the documents they view than those low in DK. The reasons of the findings, as well as implications on systems design, are discussed.
Keywords: Domain knowledge, user behaviors, relevance judgment


Predicting Task Difficulty from a User's Moment to Moment Cognitive Effort During Information Seeking BIB 23
  Michael J. Cole; Jacek Gwizdka; Chang Liu; Nicholas J. Belkin
Investigating the Effect of Visualization on User Performance of Information Systems BIB 24
  Xiaojun Yuan
Effects of Domain Knowledge on User Task Performance in a Knowledge Domain Visualization System BIB 25
  Xiaojun Yuan; Chaomei Chen; Xiangmin Zhang; Josh Avery; Tao Xu