Foraging Online: Understanding How Search Features Influence the Development of Information Search Tactics and Strategies

Online information search behaviour are increasingly pervasive and important in the current era of big data. The design of search features that accommodate to information search behaviour relies on an extensive understanding of how searchers develop search tactics and search strategies. Through the lens of foraging theory, I argue the each type of search features enables a specific search tactic, that is, how searchers advance their search with their minds and actions in accord to the inherent constraints posed by a certain search feature. Furthermore, I hypothesize that the search tactics adopted by a searcher influence his/her search strategy, meaning the planning of the whole search process, and ultimately determines the search outcome. To empirically validate the hypothesis posited in this proposal, I developed an experimental restaurant review website with four contemporary search features implemented. Real information of 1079 restaurants in San Franciscon along with about 268k reviews for these restaurants written by nearly 91k dinners are scraped to populate this website. Future experiment is planned to collect participants’ objective search behavioural data as well as their quantitative and qualitative feedback regarding the search process in order to triangulate my hypotheses

Integrating tradeoff support in product search tools for E-commerce sites

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57% after performing tradeoff tasks. Tradeoff support also significantly increased users' confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface. Copyright 2005 ACM

Designing Emergency Response Dispatch Systems for Better Dispatcher Performance

Emergency response systems are a relatively new and important area of research in the information systems community. While there is a growing body of literature in this research stream, human-computer interaction (HCI) issues concerning the design of emergency response system interfaces have received limited attention. Emergency responders often work in time pressured situations and depend on fast access to key information. One of the problems studied in HCI research is the design of interfaces to improve user information selection and processing performance. Based on cue-summation theory and research findings on parallel processing, associative processing, and hemispheric differences in information processing, this study proposes that information selection of target information in an emergency response dispatch application can be improved by using supplementary cues. Color-coding and sorting are proposed as relevant cues that can improve processing performance by providing prioritization heuristics. An experimental emergency response dispatch application is developed, and user performance is tested under conditions of varying complexity and time pressure. The results suggest that supplementary cues significantly improve performance, with better results often obtained when both cues are used. Additionally, the use of these cues becomes more beneficial as time pressure and task complexity increase

Evaluating product search and recommender systems for E-commerce environments

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyses

User decision improvement and trust building in product recommender systems

As online stores are offering an almost unlimited shelf space, users must increasingly rely on product search and recommender systems to find their most preferred products and decide which item is the truly best one to buy. However, much research work has emphasized on developing and improving the underlying algorithms whereas many of the user issues such as preference elicitation and trust formation received little attention. In this thesis, we aim at designing and evaluating various decision technologies, with emphases on how to improve users' decision accuracy with intelligent preference elicitation and revision tools, and how to build their competence-inspired subjective constructs via trustworthy recommender interfaces. Specifically, two primary technologies are proposed: one is called example critiquing agents aimed to stimulate users to conduct tradeoff navigation and freely specify feedback criteria to example products; another termed as preference-based organization interfaces designed to take two roles: explaining to users why and how the recommendations are computed and displayed, and suggesting critique suggestions to guide users to understand existing tradeoff potentials and to make concrete decision navigations from the top candidate for better choices. To evaluate the two technologies' true performance and benefits to real-users, an evaluation framework was first established, that includes important assessment standards such as the objective/subjective accuracy-effort measures and trust-related subjective aspects (e.g., competence perceptions and behavioral intentions). Based on the evaluation framework, a series of nine experiments has been conducted and most of them were participated by real-users. Three user studies focused on the example critiquing (EC) agent, which first identified the significant impact of tradeoff process with the help of EC on users' decision accuracy improvement, and then in depth explored the advantage of multi-item strategy (for critiquing coverage) against single-item display, and higher user-control level reflected by EC in supporting users to freely compose critiquing criteria for both simple and complex tradeoffs. Another three experiments studied the preference-based organization technique. Regarding its explanation role, a carefully conducted user survey and a significant-scale quantitative evaluation both demonstrated that it can be likely to increase users' competence perception and return intention, and reduce their cognitive effort in information searching, relative to the traditional "why" explanation method in ranked list views. In addition, a retrospective simulation revealed its superior algorithm accuracy in predicting critiques and product choices that real-users intended to make, in comparison with other typical critiquing generation approaches. Motivated by the empirically findings in terms of the two technologies' respective strengths, a hybrid system has been developed with the purpose of combining them into a single application. The final three experiments evaluated its two design versions and particularly validated the hybrid system's universal effectiveness among people from different types of cultural backgrounds: oriental culture and western culture. In the end, a set of design guidelines is derived from all of the experimental results. They should be helpful for the development of a preference-based recommender system, making it capable of practically benefiting its users in improving decision accuracy, expending effort they are willing to invest, and even promoting trust in the system with resulting behavioral intentions to purchase chosen products and return to the system for repeated uses