Investigating the role of in-situ user expectations in Web search

Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems.     

The influence of task and gender on search and evaluation behavior using Google

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.     

An elaborated model of social search

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon’s Mechanical Turk service suggest that social interactions play an important role throughout the search process. A second survey of also 150 users, focused instead on difficulties encountered during searches, suggests similar conclusions. These social interactions range from highly coordinated collaborations with shared goals to loosely coordinated collaborations in which only advice is sought. Our main contribution is that we have integrated models from previous work in sensemaking and information-seeking behavior to present a canonical social model of user activities before, during, and after a search episode, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers.     

个性化信息推荐服务中用户潜在兴趣挖掘研究

如何准确分析用户行为,向用户提供满意的网页信息,一直以来都是个性化信息推荐系统设计的目标。本文在分析现有个性化信息推荐模型的基础上,针对以往研究在推荐兴趣时仅根据语义相关度进行协助性信息推荐,而忽略用户行为规律所包含的潜在兴趣信息的不足,尝试提出一个结合Web语义挖掘和FP-tree规则发现技术的个性化信息推荐模型。该模型利用本体对语义的明确化描述,在挖掘用户行为信息时获取用户兴趣偏好的语义信息,并利用FP-tree技术根据以获取的语义信息推理出用户兴趣行为模式,从而在信息推荐时不仅能准确理解用户兴趣偏好,也能根据用户潜在兴趣规律,推荐给用户更全面的网页信息。     

ISP视域下的信息搜索用户学习过程演化模型构建

【目的/意义】对于理解“搜索即学习”理论机制,以及支持用户学习需求的信息系统的设计,融合学习与社交体验功能的检索工具的优化具有启示。【方法/过程】将ISP模型作为理论基础,综合使用日记研究与关键事件技术方法,讨论了信息搜索用户学习过程中的行动、情感与认知变化的基本规律,并基于此构建了信息搜索用户学习过程演化模型。【结果/结论】在启动、选择、探索、形成、收集与呈现六个阶段当中,信息搜索用户在行动、情感与认知上均具有非常丰富的表现。特别地,在启动阶段,出现了目标明确和目标模糊两种事件类型;在形成阶段,用户会结合对搜索结果的认识与学习需求的满足程度形成“提前上岸”与“乘风破浪”两种情形。社会化搜索、跨设备搜索、跨系统搜索等已经成为“搜索即学习”活动中的普遍现象。【创新/局限】在“搜索即学习”研究情境下重新检验了ISP模型,但对影响用户情感与认知的具体因素等的分析与解释还有待深入。     

Combining Non-sampling and Self-attention for Sequential Recommendation

With the rapid development of social media and big data technology, user’s sequence behavior information can be well recorded and preserved on different media platforms. It is crucial to model the user preference through mining their sequential behaviors. The goal of sequential recommendation is to predict what a user may interact with in the next moment based on the user’s historical record of interactive sequence. However, existing sequential recommendation methods generally adopt a negative sampling mechanism (e.g. random and uniform sampling) for the pairwise learning, which brings the defect of insufficient training to the model, and decrease the evaluation performance of the entire model. Therefore, we propose a Non-sampling Self-attentive Sequential Recommendation (NSSR) model that combines non-sampling mechanism and self-attention mechanism. Under the premise of ensuring the efficient training of the model, NSSR model takes all pairs in the training set as training samples, so as to achieve the goal of fully training the model. Specifically, we take the interactive sequence as the current user representation, and propose a new loss function to implement the non-sampling training mechanism. Finally, the state-of-the-art result is achieved on three public datasets, Movielens-1M, Amazon Beauty and Foursquare_TKY, and the recommendation performance increase by about 29.3%, 25.7% and 42.1% respectively.     

An evaluation of adaptive filtering in the context of realistic task-based information exploration

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.     

Model-driven formative evaluation of exploratory search: A study under a sensemaking framework

The evaluation of exploratory search relies on the ongoing paradigm shift from focusing on the search algorithm to focusing on the interactive process. This paper proposes a model-driven formative evaluation approach, in which the goal is not the evaluation of a specific system, per se, but the exploration of new design possibilities. This paper gives an example of this approach where a model of sensemaking was used to inform the evaluation of a basic exploratory search system(s) in the context of a sensemaking task. The model suggested that, rather than just looking at simple search performance measures, we should examine closely the interwoven, interactive processes of both representation construction and information seeking. Participants were asked to make sense of an unfamiliar topic using an augmented query-based search system. The processes of representation construction and information seeking were captured and analyzed using data from experiment notes, interviews, and a system log. The data analysis revealed users’ sources of ideas for structuring representations and a tightly coupled relationship between search and representation construction in their exploratory searches. For example, users strategically used search to find useful structure ideas instead of just accumulating information facts. Implications for improving current search systems and designing new systems are discussed.     

Specifics of information retrieval for young users: A survey

In this paper, we present the state of the art in the field of information retrieval that is relevant for understanding how to design information retrieval systems for children. We describe basic theories of human development to explain the specifics of young users, i.e., their cognitive skills, fine motor skills, knowledge, memory and emotional states in so far as they differ from those of adults. We derive the implications these differences have on the design of information retrieval systems for children. Furthermore, we summarize the main findings about children’s search behavior from multiple user studies. These findings are important to understand children’s information needs, their search strategies and usage of information retrieval systems. We also identify several weaknesses of previous user studies about children’s information-seeking behavior. Guided by the findings of these user studies, we describe challenges for the design of information retrieval systems for young users. We give an overview of algorithms and user interface concepts. We also describe existing information retrieval systems for children, in specific web search engines and digital libraries. We conclude with a discussion of open issues and directions for further research. The survey provided in this paper is important both for designers of information retrieval systems for young users as well as for researchers who start working in this field.     

Image searching on the Excite Web search engine

A growing body of research is beginning to explore the information-seeking behavior of Web users. The vast majority of these studies have concentrated on the area of textual information retrieval (IR). Little research has examined how people search for non-textual information on the Internet, and few large-scale studies has investigated visual information-seeking behavior with general-purpose Web search engines. This study examined visual information needs as expressed in users’ Web image queries. The data set examined consisted of 1,025,908 sequential queries from 211,058 users of Excite, a major Internet search service. Twenty-eight terms were used to identify queries for both still and moving images, resulting in a subset of 33,149 image queries by 9855 users. We provide data on: (1) image queries – the number of queries and the number of search terms per user, (2) image search sessions – the number of queries per user, modifications made to subsequent queries in a session, and (3) image terms – their rank/frequency distribution and the most highly used search terms. On average, there were 3.36 image queries per user containing an average of 3.74 terms per query. Image queries contained a large number of unique terms. The most frequently occurring image related terms appeared less than 10% of the time, with most terms occurring only once. We contrast this to earlier work by P.G.B. Enser, Journal of Documentation 51 (2) (1995) 126–170, who examined written queries for pictorial information in a non-digital environment. Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed.     

Asking Clarifying Questions: To benefit or to disturb users in Web search?

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.     

Why do I take deviant disclosure behavior on internet platforms? An explanation based on the neutralization theory

Internet platforms increasingly rely on personal data to maintain user base and attract potential users. However, users might misrepresent their personal information in response to the requests when they expect their personal information to be used for unstated purposes. Misrepresenting of personal information (MPI), as a regular deviant behavior on online platforms, leads to data noises and service failures and needs further investigation. Drawing on the neutralization theory and online scenario survey, this study constructs a model to examine the factors of MPI. The results show that techniques of neutralization, such as denial of injury, defense of necessity, metaphor of the ledger, and avoidance of greater harm, are positively associated with users’ intention of MPI. In particular, avoidance of greater harm has a stronger effect on MPI intention than other neutralization techniques. Furthermore, we also explore the contingent role of personality traits. The results show that extraversion negatively moderates the relationships between denial of injury, avoidance of greater harm and MPI, whereas neuroticism plays a positive role in moderating the relationships between denial of injury, metaphor of the ledger, avoidance of greater harm and MPI. Moreover, post-hoc analysis results show that users do some differernces regarding justify their deviant disclosure behavior by using neutralization techniques between e-commerce platform and social networking site. Focusing on the dark side of disclosure behavior, this study enriches the current literature by applying neutralization theory and provides a new direction for academia and Internet practitioners to understand users’ deviant disclosure behavior.     

Design and evaluation of awareness mechanisms in CiteSeer

Awareness has been extensively studied in human computer interaction (HCI) and computer supported cooperative work (CSCW). The success of many collaborative systems hinges on effectively supporting awareness of different collaborators, their actions, and the process of creating shared work products. As digital libraries are increasingly becoming more than just repositories for information search and retrieval – essentially fostering collaboration among its community of users – awareness remains an unexplored research area in this domain. We are investigating awareness mechanisms in CiteSeer, a scholarly digital library for the computer and information science domain. CiteSeer users can be notified of new publication events (e.g., publication of a paper that cites one of their papers) using feeds as notification systems. We present three cumulative user studies – requirements elicitation, prototype evaluation, and naturalistic study – in the context of supporting CiteSeer feeds. Our results indicate that users prefer feeds that place target items in query-relevant contexts, and that preferred context varies with type of publication event. We found that users integrated feeds as part of their broader, everyday activities and used them as planning tools to collaborate with others.     

Impact of response latency on sponsored search

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.     

Mining user interests over active topics on social networks

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.     

Adaptive visualization for exploratory information retrieval

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.     

在线健康社区用户复合信息行为的实证研究——信息共享和信息搜寻同步的视角

【目的/意义】信息共享和信息搜寻是用户参与在线健康社区的主要方式,在参与过程中,用户往往会出于 不同目的同步实施信息共享和信息搜寻的复合信息行为,探讨影响用户同步实施两种行为的因素,有助于帮助在 线健康社区精准把握用户需求,进一步优化提升服务策略。【方法/过程】通过结合自我决定理论和TAM模型,构建 在线健康社区用户信息共享与搜寻行为意愿影响因素模型。通过发放问卷收集数据并运用结构方程模型对研究 假设进行验证。【结果/结论】研究结果表明健康焦虑对信息搜寻意愿有显著影响,情感需要对信息共享意愿有显著 影响。此外,用户信息共享意愿对信息共享和搜寻行为均有正向影响,而用户的信息搜寻意愿仅显著影响搜寻行 为。【创新/局限】丰富了在线健康社区用户信息行为研究领域的理论框架,为在线健康社区用户行为研究提供了新 的视角。未来的相关研究将会针对某一特定社区用户进行更深层次的探讨。      

Strokes of insight: User intent detection and kinematic compression of mouse cursor trails

Web users often have a specific goal in mind comprising various stages that are reflected, as executed, by their mouse cursor movements. Therefore, is it possible to detect automatically which parts of those movements bear any intent and discard the parts that have no intent? Can we estimate the intent degree of the non-discarded parts? To achieve this goal, we tap into the Kinematic Theory and its associated Sigma-Lognormal model (ΣΛM). According to this theory, the production of a mouse cursor movement requires beforehand the instantiation of an action plan. The ΣΛM models such an action plan as a sequence of strokes’ velocity profiles, one stroke at a time, providing thus a reconstruction of the original mouse cursor movement. When a user intent is clear, the pointing movement is faster and the cursor movement is reconstructed almost perfectly, while the reverse is observed when the user intent is unclear.We analyzed more than 10,000 browsing sessions comprising about 5 million of data points, and compared different segmentation techniques to detect discrete cursor chunks that were then reconstructed with the ΣΛM. Our main contribution is thus a novel methodology to automatically tell chunks with and without intention apart. We also contribute with kinematic compression, a novel application to compress mouse cursor data while preserving most of the original information. Ultimately, this work enables a deeper understanding of mouse cursor movements production, providing an informed means to gain additional insight about users’ browsing behavior.     

信息用户成长模型的构建

在介绍信息用户相关研究模型的基础上,分析了目前信息用户研究模型存在的不足,并构建了信息用户成长模型,以更好地体现信息用户为中心的理念.该模型是通过系统地考察信息行为、信息意识、信息环境的影响等因素的基础上建立的.该模型能够更好地反映信息环境对用户的长远影响.