Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Abstract

Two problems may arise when an intelligent (recommender) system elicits users' preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users' mental preference models. Giving exact numbers, e.g., such as "I like 30 days of vacation 2.5 times better than 28 days" is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

Inspectability and control in social recommenders

“…Other common methods of control include rating items [13,39] and assigning weights to item attributes [18,29]. Research shows that the choice between these different methods has a substantial impact on the user experience [5,27,29].…”

User effort vs. accuracy in rating-based elicitation

“…Hence a wide amount of studies have explored the elicitation process, trying to understand how the construction of preferences process takes place [26], which questions to ask a new user [27], which items to propose [26], in which form and order [26]. For more exhaustive reviews of these issues, the reader is referred to [5].…”

“…A number of works have analyzed and compared the cognitive effort related to non-rating-based sign-up processes (tagging items [12], elicitation of user preferences on product features [2], personality quiz [15], and affective feedback [26]). In the experiments described in [26], for example, cognitively less demanding elicitation methods were perceived low in effort and high in liking.…”

Preference Elicitation and Negotiation in a Group Recommender System

“…Our system supports remote online negotiation, although the approach can also be adapted to co -located settings. Each user can specify an individual preference model by freely adding d esired features, using an explicit preference elicitation approach [27]. The individual preferences are then aggregated to form the group preference model and to determine an initial set of recommendations.…”