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1.
Modern information-seeking systems are becoming more interactive, mainly through asking Clarifying Questions (CQs) to refine users’ information needs. System-generated CQs may be of different qualities. However, the impact of asking multiple CQs of different qualities in a search session remains underexplored. Given the multi-turn nature of conversational information-seeking sessions, it is critical to understand and measure the impact of CQs of different qualities, when they are posed in various orders. In this paper, we conduct a user study on CQ quality trajectories, i.e., asking CQs of different qualities in chronological order. We aim to investigate to what extent the trajectory of CQs of different qualities affects user search behavior and satisfaction, on both query-level and session-level. Our user study is conducted with 89 participants as search engine users. Participants are asked to complete a set of Web search tasks. We find that the trajectory of CQs does affect the way users interact with Search Engine Result Pages (SERPs), e.g., a preceding high-quality CQ prompts the depth users to interact with SERPs, while a preceding low-quality CQ prevents such interaction. Our study also demonstrates that asking follow-up high-quality CQs improves the low search performance and user satisfaction caused by earlier low-quality CQs. In addition, only showing high-quality CQs while hiding other CQs receives better gains with less effort. That is, always showing all CQs may be risky and low-quality CQs do disturb users. Based on observations from our user study, we further propose a transformer-based model to predict which CQs to ask, to avoid disturbing users. In short, our study provides insights into the effects of trajectory of asking CQs, and our results will be helpful in designing more effective and enjoyable search clarification systems.  相似文献   

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3.
This paper investigates the influence of user characteristics (e.g. search experience and cognitive skills) on user effectiveness. A user study was conducted to investigate this effect, 56 participants completed searches for 56 topics using the TREC test collection. Results indicated that participants with search experience and high cognitive skills were more effective than those with less experience and slower perceptual abilities. However, all users rated themselves with the same level of satisfaction with the search results despite the fact they varied substantially in their effectiveness. Therefore, information retrieval evaluators should take these factors into consideration when investigating the impact of system effectiveness on user effectiveness.  相似文献   

4.
This study aims to explore the relationships between user interaction and digital libraries (DLs) evaluation. User interaction is a multi-dimensional construct and recognized as three dimensions in this study, as user interaction with: information resource; interface; and, tasks. DL evaluation is considered from the user's perspective and defined as users’ perception of DL performance from different perspectives, including the support of DL's interaction design to user interaction (labeled as interaction-design-based (IDB) evaluation), the support of task completion (labeled as task-based evaluation), and a DL's overall performance (labeled as overall evaluation). An experiment with 48 participants was conducted using the China National Knowledge Infrastructure (CNKI (http://cnki.net/), the most widely used digital library in China). Participants searched for four simulated work tasks and one real work task during the experiment, subsequently evaluating their interaction with information resource, interface, and tasks, and DL performance from different perspectives before or after the search. Correlation analysis and stepwise regression analysis were conducted to examine the relationships. The results indicate that a list of factors related to different dimensions of user interaction can significantly predict or be correlated to users’ evaluation of DL performance from different perspectives, including appropriateness, rich and valid links, reasonable page layout, salience of topics, search task difficulty, well-organized web site, easy to learn, accessibility, usefulness, familiarity with task procedure, etc. These factors surface as the most critical criteria for DL evaluation. Based on the results, an integrated DL evaluation framework is developed. The study adds new knowledge about how tasks affect DL evaluation. It has implications for improving the efficiency of DL evaluation and helping DL developers design DLs to better support users’ interaction, task completion, and their overall experience with DLs.  相似文献   

5.
In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval.  相似文献   

6.
Recent research in the human computer interaction and information retrieval areas has revealed that search response latency exhibits a clear impact on the user behavior in web search. Such impact is reflected both in users’ subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results they engage with. However, a similar impact analysis has been missing so far in the context of sponsored search. Since the predominant business model for commercial search engines is advertising via sponsored search results (i.e., search advertisements), understanding how response latency influences the user interaction with the advertisements displayed on the search engine result pages is crucial to increase the revenue of a commercial search engine. To this end, we conduct a large-scale analysis using query logs obtained from a commercial web search. We analyze the short-term and long-term impact of search response latency on the querying and clicking behaviors of users using desktop and mobile devices to access the search engine, as well as the corresponding impact on the revenue of the search engine. This analysis demonstrates the importance of serving sponsored search results with low latency and provides insight into the ad serving policy of commercial search engines to ensure long-term user engagement and search revenue.  相似文献   

7.
The user experience is an integral component of interactive information retrieval (IIR). However, there is a twofold problem in its measurement. Firstly, while many IIR studies have relied on a single dimension of user feedback, that of satisfaction, experience is a much more complex concept. IIR in general, and exploratory search more specifically, are dynamic, multifaceted experiences that evoke pragmatic and hedonic needs, expectations, and outcomes that are not adequately captured by user satisfaction. Secondly, questionnaires, which are typically the means in which user’s attitudes and perceptions are measured, are not typically subjected to rigorous reliability and validity testing. To address these issues, we administered the multidimensional User Engagement Scale (UES) in an exploratory search environment to assess users’ perceptions of the Perceived Usability (PUs), Aesthetics (AE), Novelty (NO), Felt Involvement (FI), Focused Attention (FA), and Endurability (EN) aspects of the experience. In a typical laboratory-style study, 381 participants performed three relatively complex search tasks using a novel search interface, and responded to the UES immediately upon completion. We used Principal Axis Factor Analysis and Multiple Regression to examine the factor structure of UES items and the relationships amongst factors. Results showed that three of the six sub-scales (PUs, AE, FA) were stable, while NO, FI and EN merged to form a single factor. We discuss recommendations for revising and validating the UES in light of these findings.  相似文献   

8.
An individual's Web search behavior can be influenced by a number of factors, including features and functions of a search engine as well as search education. In contrast to the long-lasting attention to the algorithm and interface dimensions of search, there is a lack of research concerned with the potential effects of user education on search behavior. To address this gap, we ran a three-session field-lab-combined study to examine the effects of user education from two distinct sources – peer advice and cognitive authority (operationalized as video-based student's advice and expert's advice respectively) – on Web search behavior in two different search task scenarios (i.e., factual specific and factual amorphous tasks). We also tested if these behavioral effects persist for a short period of time when the explicit search tips are removed. Using 185 task session data generated by 31 participants in two field and one lab sessions, this study demonstrates that: (1) both peer advice and cognitive authority are effective in stimulating immediate behavioral changes in Web search; (2) the immediate behavioral impact of search advice is broader in factual amorphous task than in factual specific task; (3) framing search tips as the advice from cognitive authority is more likely to generate continuing, short-term effects on Web search behaviors. This research has implications for the design of task-aware user education as well as the study of users’ interactions with IR systems in general.  相似文献   

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10.
In this paper, we explore the effects of individual pressure level and time constraint on searchers' behaviors and their assessment of search experience within the framework of interactive information retrieval. A user experiment was conducted in which 40 participants individually searched for information in a laboratory setting under two conditions: with time constraint (TC) and with no time constraint (NTC). Participants filled in a Perceived Stress Scale questionnaire to measure their chronic pressure value (subjective stress), and their pressure value was recorded as their individual characteristic. The results showed that the more chronic pressure the searcher has, the more search efforts they devote, including more time in searching and more time to complete the search tasks, especially when there was no time constraint. Time constraint and searchers’ pressure value had a significant effect on users’ numbers of scrolling actions per minute. The results indicate that when given a time constraint, searchers with higher-pressure values tend to lower their reading or scanning speed, while searchers with lower-pressure values tend to accelerate their reading or scanning speed. The results suggested different people would react to the time condition change in different ways, especially people with higher pressure. Therefore, it is necessary to examine users’ search behaviors in person-in-situation frameworks to analyze the effects of contextual factors on users. This study contributes to our knowledge of how contextual factors and individual characteristics affect searchers’ behaviors and have implications for the design of IIR systems.  相似文献   

11.
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.  相似文献   

12.
Exploratory search increasingly becomes an important research topic. Our interests focus on task-based information exploration, a specific type of exploratory search performed by a range of professional users, such as intelligence analysts. In this paper, we present an evaluation framework designed specifically for assessing and comparing performance of innovative information access tools created to support the work of intelligence analysts in the context of task-based information exploration. The motivation for the development of this framework came from our needs for testing systems in task-based information exploration, which cannot be satisfied by existing frameworks. The new framework is closely tied with the kind of tasks that intelligence analysts perform: complex, dynamic, and multiple facets and multiple stages. It views the user rather than the information system as the center of the evaluation, and examines how well users are served by the systems in their tasks. The evaluation framework examines the support of the systems at users’ major information access stages, such as information foraging and sense-making. The framework is accompanied by a reference test collection that has 18 tasks scenarios and corresponding passage-level ground truth annotations. To demonstrate the usage of the framework and the reference test collection, we present a specific evaluation study on CAFÉ, an adaptive filtering engine designed for supporting task-based information exploration. This study is a successful use case of the framework, and the study indeed revealed various aspects of the information systems and their roles in supporting task-based information exploration.  相似文献   

13.
The purpose of the current study is to identify the user criteria and data-driven features, both textual and non-textual, for assessing the quality of answers posted on social questioning and answering sites (social Q&A) across four different knowledge domains—Science, Technology, Art and Recreation. A comprehensive review of literature on quality assessment of information produced in social contexts was carried out to develop the theoretical framework for the current study. A total of 23 user criteria and 24 data features were proposed and tested with high-quality answers obtained from four social Q&A sites in Stack Exchange. Findings indicate that content-related criteria and user and review features were the most frequently used in quality assessments, while the importance of user criteria and data features was variable across the knowledge domains. In the Technology Q&A site containing mostly self-help questions, the utility class was the most frequently used group of criteria. The popularity of the socio-emotional class was more apparent in discussion-oriented topic categories such as Art and Recreation, where people seek others’ opinions or advice. Users of Art and Recreation Q&A sites in Stack Exchange appear to place more value on answerers’ efforts and time, good attitudes or manners, personal experience, and the same taste. The importance of user features and the emphasis on answerer's expertise on the Science Q&A site was observed. Examining the connection or gap between user quality criteria and data features across the knowledge domains could help to better understand users’ evaluation behaviors for their preferred answers, and identify the potential of social Q&A for user education/intervention in answer quality evaluation. This examination also offers practical guidance for designing more effective social Q&A platforms, considering how to customize community support systems, motivate contributions, and control content quality.  相似文献   

14.
Information need is one of the most fundamental aspects of information seeking, which traditionally conceptualizes as the initiation phase of an individual’s information seeking behavior. However, the very elusive and inexpressible nature of information need makes it hard to elicit from the information seeker or to extract through an automated process. One approach to understanding how a person realizes and expresses information need is to observe their seeking behaviors, to engage processes with information retrieval systems, and to focus on situated performative actions. Using Dervin’s Sense-Making theory and conceptualization of information need based on existing studies, the work reported here tries to understand and explore the concept of information need from a fresh methodological perspective by examining users’ perceived barriers and desired helps in different stages of information search episodes through the analyses of various implicit and explicit user search behaviors. In a controlled lab study, each participant performed three simulated online information search tasks. Participants’ implicit behaviors were collected through search logs, and explicit feedback was elicited through pre-task and post-task questionnaires. A total of 208 query segments were logged, along with users’ annotations on perceived problems and help. Data collected from the study was analyzed by applying both quantitative and qualitative methods. The findings identified several behaviors – such as the number of bookmarks, query length, number of the unique queries, time spent on search results observed in the previous segment, the current segment, and throughout the session – strongly associated with participants’ perceived barriers and help needed. The findings also showed that it is possible to build accurate predictive models to infer perceived problems of articulation of queries, useless and irrelevant information, and unavailability of information from users’ previous segment, current segment, and whole session behaviors. The findings also demonstrated that by combining perceived problem(s) and search behavioral features, it was possible to infer users’ needed help(s) in search with a certain level of accuracy (78%).  相似文献   

15.
Whether to deal with issues related to information ranking (e.g. search engines) or content recommendation (on social networks, for instance), algorithms are at the core of processes that select which information is made visible. Such algorithmic choices have a strong impact on users’ activity de facto, and therefore on their access to information. This raises the question of how to measure the quality of the choices algorithms make and their impact on users. As a first step in that direction, this paper presents a framework with which to analyze the diversity of information accessed by users in the context of musical content.The approach adopted centers on the representation of user activity through a tripartite graph that maps users to products and products to categories. In turn, conducting random walks in this structure makes it possible to analyze how categories catch users’ attention and how this attention is distributed. Building upon this distribution, we propose a new index referred to as the (calibrated) herfindahl diversity, which is aimed at quantifying the extent to which this distribution is diverse and representative of existing categories.To the best of our knowledge, this paper is the first to connect the output of random walks on graphs with diversity indexes. We demonstrate the benefit of such an approach by applying our index to two datasets that record user activity on online platforms involving musical content. The results are threefold. First, we show that our index can discriminate between different user behaviors. Second, we shed some light on a saturation phenomenon in the diversity of users’ attention. Finally, we show that the lack of diversity observed in the datasets derives from exogenous factors related to the heterogeneous popularity of music styles, as opposed to internal factors such as recurrent user behaviors.  相似文献   

16.
Social Networking Sites (SNSs) play an important role in our daily lives and the number of their users increases regularly. To understand how users can be satisfied in the complex digital environment of SNSs, this study examines how motivations and emotions combine with each other to explain high satisfaction. Users’ motivations comprise four attributes, entertainment, information, social-psychological, and convenience. Emotions are divided into their two main categories, that is positive and negative emotions. We draw on complexity and configuration theories, present a conceptual model along with propositions and perform a fuzzy-set qualitative comparative analysis (fsQCA). Through an empirically study with 582 SNSs users, we present eight combinations (configurations) of motivations and emotions that lead to high satisfaction, which highlight the role of high convenience, followed by entertainment and information motivations in being satisfied with SNSs. High satisfaction can be achieved both when positive and negative emotions are high and low, depending on how they combine users’ motivations. None of the factors are indispensable to explain high satisfaction on their own, instead they are insufficient but necessary parts of the causal combinations that explain high satisfaction. This study contributes in SNSs literature by extending current knowledge on how motivations and emotions combine to increase satisfaction, and by identifying specific patterns of users for whom these factors are important and influence greatly their satisfaction.  相似文献   

17.
To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users’ information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.  相似文献   

18.
In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.  相似文献   

19.
We inductively develop a model of the commercialization process for new products or services user entrepreneurs undertake when entering an industry while drawing on proprietary technology developed in another industry. Extending the growing field of user entrepreneurship, we identify a two-phase approach to industry entry by user entrepreneurs who start “under the radar” of incumbent firms, gain experience, attract a first potential customer base, and then, in a second phase, engage in commercialization. During this process, a community of fellow users is of major importance for the entrepreneur, serving as a knowledge pool for skills development and experimentation with different commercialization paths. We study a nascent group of firms founded by users of video games who became entrepreneurs on entering the animation industry by producing Machinima, a new film genre characterized by shooting film in video games. We explain how user entrepreneurs gain access to complementary assets (video games) for their new use (shooting film), how they deal with intellectual property issues when using other firms’ assets, and how user entrepreneurs combine domain knowledge about film production with their experience in video games and the art of Machinima. Our propositions hold implications for management and policy.  相似文献   

20.
User related issues have long been broadly discussed in the information system development (ISD) project research area. In this study, we focus on user risk and identify two risk countering approaches to demonstrate how to deal with user risk and its negative impact on ISD projects. We hypothesize that (1) user risk has a negative impact on project performance, (2) users’ bond with the project and the development team can help reduce user risk, and (3) developers’ task knowledge and vertical coordination can ease the negative impact of user risk and increase project performance. A quantitative approach with survey data collected from 240 practitioners confirmed our hypotheses. In addition, we interviewed seven developers and three user representatives to complete our understanding of this issue. Implications for academia and practitioners are discussed at the end of this paper. Suggestions for future research directions are also provided.  相似文献   

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