This paper presents a visual interface developed on the basis of control and transparency to elicit preferences in the scientific and cultural domain. Preference elicitation is a recognized challenge in user modeling for personalized recommender systems. The amount of feedback the user is willing to provide depends on how trustworthy the system seems to be and how invasive the elicitation proce...

Preference elicitation is important for any computerized system advising users about choices. Recommender systems aim to propose interesting material to users. Therefore, they must first gather user preferences. Negotiation support systems can only give meaningful bidding advice based on users’ preferences regarding negotiable issues and interests. In general, the more detail users are willing ...

One of the most challenging goals of recommender systems is to infer the preferences of users through the observation of their actions. Those preferences are essential to obtain a satisfactory accuracy in the recommendations. Preference learning is especially difficult when attributes of different kinds (numeric or linguistic) intervene in the problem, and even more when they take multiple poss...

Knowing tourists’ individual preferences provides the possibility to offer personalised tours. The challenge is to capture these preferences using a mobile device. During a field study in Görlitz three methods for preference elicitation were evaluated. The results served to clarify fundamental questions en route to developing a personal tour guide: 1) Is it possible to seed interest profiles in...

Creating user preference models has become an important endeavor for HCI. Forming a preference profile is a constructive process in the user’s mind depending on use context as well as a user’s thinking and information processing style. We believe a one-style-fits-all approach to the design of these interfaces is not sufficient in supporting users in constructing an accurate profile. We present ...

Research from behavioral psychology and experimental economics asserts that individuals construct preferences on a case-by-case basis when called to make a decision. A common, implicit assumption in engineering design is that user preferences exist a priori. Thus, preference elicitation methods used in design decision making can lead to preference inconsistencies across elicitation scenarios. T...

This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of ̄ items. The system has a rich representation of preference, dis...

Preference learning has recently gained significant attention in the machine learning community. This is mainly due to its increasing applications in real-world problems such as recommender systems. In this paper, we investigate a Gaussian process framework for learning preferences that uses Expectation Propagation (EP) as its main inference method. This framework is capable of using the collab...

In this study, we conducted an online survey and collected 86 reliable responses on both a personality assessment inventory and ratings retail products ratings, with the aim of investigating whether personality characteristics have an impact on user rating behaviors. Besides personality factors, another four independent variables (i.e., age, gender, previous experience on using recommenders and...