Hybrid critiquing-based recommender systems

The “Myth of the Heavenly Cow” is the conventional title of an Egyptian mythological narrative that relates how humanity once rebelled against the sun god and how thereupon the sun god reorganized the cosmos. The narrative is embedded in the so-called Book of the Heavenly Cow, which is preserved in several versions dating to the New Kingdom. It is an etiological myth explaining the origins of certain natural phenomena and religious festivals as well as legitimizing the institution of Egyptian kingship

Interaction design guidelines on critiquing-based recommender systems

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage—one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid—system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender syste

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

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Design and evaluation issues for user-centric online product search

Nowadays more and more people are looking for products online, and a massive amount of products are being sold through e-commerce systems. It is crucial to develop effective online product search tools to assist users to find their desired products and to make sound purchase decisions. Currently, most existing online product search tools are not very effective in helping users because they ignore the fact that users only have limited knowledge and computational capacity to process the product information. For example, a search tool may ask users to fill in a form with too many detailed questions, and the search results may either be too minimal or too vast to consider. Such system-centric designs of online product search tools may cause some serious problems to end-users. Most of the time users are unable to state all their preferences at one time, so the search results may not be very accurate. In addition, users can either be impatient to view too much product information, or feel lost when no product appears in the search results during the interaction process. User-centric online product search tools can be developed to solve these problems and to help users make buying decisions effectively. The search tool should have the ability to recommend suitable products to meet the user's various preferences. In addition, it should help the user navigate the product space and reach the final target product without too much effort. Furthermore, according to behavior decision theory, users are likely to construct their preferences during the decision process, so the tool should be designed in an interactive way to elicit users' preferences gradually. Moreover, it should be decision supportive for users to make accurate purchasing decisions even if they don't have detail domain knowledge of the specific products. To develop effective user-centric online product search tools, one important task is to evaluate their performance so that system designers can obtain prompt feedback. Another crucial task is to design new algorithms and new user interfaces of the tools so that they can help users find the desired products more efficiently. In this thesis, we first consider the evaluation issue by developing a simulation environment to analyze the performance of generic product search tools. Compared to earlier evaluation methods that are mainly based on real-user studies, this simulation environment is faster and less expensive. Then we implement the CritiqueShop system, an online product search tool based on the well-known critiquing technique with two aspects of novelties: a user-centric compound critiquing generation algorithm which generates search results efficiently, and a visual user interface for enhancing user's satisfaction degree. Both the algorithm and the user interface are validated by large-scale comparative real-user studies. Moreover, the collaborative filtering approach is widely used to help people find low-risk products in domains such as movies or books. Here we further propose a recursive collaborative filtering approach that is able to generate search results more accurately without requiring additional effort from the users

Preference-based Search using Example-Critiquing with Suggestions

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%

Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker

Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition benchmarks and directly applied to situated interactions. However, the conditions in situated human-robot interactions differ significantly from the training data and settings of these models. Thus, there is a need to deepen our understanding of how AC solutions can be best leveraged, customised and applied for situated HRI. This paper, while critiquing the existing practices, presents four critical lessons to be noted by the hitchhiker when applying AC for HRI research. These lessons conclude that: (i) The six basic emotions categories are irrelevant in situated interactions, (ii) Affect recognition accuracy (%) improvements are unimportant, (iii) Affect recognition does not generalise across contexts, and (iv) Affect recognition alone is insufficient for adaptation and personalisation. By describing the background and the context for each lesson, and demonstrating how these lessons have been learnt, this paper aims to enable the hitchhiker to successfully and insightfully leverage AC solutions for advancing HRI research.Comment: 11 pages, 3 figures, 1 tabl

Defining the mechanisms of a cooperative computer system based on theories of cooperation

There is a growing interest in the development of computer systems that are actively involved in the tasks of the users and serve to augment the users' creativity. Cooperative computing is a major contribution to this research field. A survey of current developments in knowledge based systems led to the conclusion that there has hitherto been an absence of a formal definition of the mechanisms of cooperative computer systems based on theories of cooperation. The work in this thesis seeks to provide a full definition of cooperation derived from the behaviours of living cooperative systems. Studies on human cooperation and cooperation in the animal kingdom, established that cooperation is a dynamic behaviour; in that the interaction processes between the cooperative partners serve to facilitate the achievement of a common goal, or a set of goals that are mutually desired by the partners. Partners in cooperation are interdependent: one member's actions are contingent on another. Therefore, the underlying processes which induce and maintain cooperation were identified. These are: communication between the partners; emergence of norms and roles governing the behaviour of the cooperating members; resolution of conflicts; distributed and coordinated activities. These factors were further elucidated within the context of small problem solving groups. A model of cooperationw hich encapsulatedth esef actors was produced. From the discussionso f the advantageso f cooperationw ithin different contexts, the potential for synergy was found to be the main benefit of cooperation. The potential for achieving this synergy between a human and a computer is the main motivation for the work undertaken in this research. From the theoretical analysis of cooperation, the underlying mechanisms of a cooperative computer were successfully defined. A conceptual model of human-computer cooperation was presented. It was established that the quality of cooperation is closely associated with the nature of the task. Therefore, it is not practicable to produce a general purpose cooperative system. A specific task must be used. Creative tasks of a problem identifying and solving nature, were found to be more suitable to cooperative behaviour than others. Typical of these, and the one selected, was computer screen design. Current screen design practice was analysed, and the functional requirements and knowledge base needs of the systems were established. The underlying mechanisms of cooperation were formalised and successfully implemented within a software exemplar, named COSY. COSY exhibits the behavioural characteristics of cooperation, and utilises the knowledge of screen design to support users in the task of formatting computer screens. COSY successfully demonstrated the synergistic relationship in its cooperation with the users. It is concluded that the approach undertaken in this thesis has lead to a successful definition and implementation of the formal mechanisms of cooperation in a computer system, one which potentially enhances the innovative and creative aspects of design work

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