Engagement in proactive recommendations

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Exploring Users' Perception of Collaborative Explanation Styles

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations

Spatio-Temporal Interpolation Is Accomplished by Binocular Form and Motion Mechanisms

Spatio-temporal interpolation describes the ability of the visual system to perceive shapes as whole figures (Gestalts), even if they are moving behind narrow apertures, so that only thin slices of them meet the eye at any given point in time. The interpolation process requires registration of the form slices, as well as perception of the shape's global motion, in order to reassemble the slices in the correct order. The commonly proposed mechanism is a spatio-temporal motion detector with a receptive field, for which spatial distance and temporal delays are interchangeable, and which has generally been regarded as monocular. Here we investigate separately the nature of the motion and the form detection involved in spatio-temporal interpolation, using dichoptic masking and interocular presentation tasks. The results clearly demonstrate that the associated mechanisms for both motion and form are binocular rather than monocular. Hence, we question the traditional view according to which spatio-temporal interpolation is achieved by monocular first-order motion-energy detectors in favour of models featuring binocular motion and form detection

A Collaborative Constraint-based Meta-level Recommender

Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user’s nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases