There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user's emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user's experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users' emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.
This paper proposes a novel approach to automatic estimation of attention of students during lectures in the classroom. The approach uses 2D and 3D data obtained by the Kinect One sensor to build a feature set characterizing both facial and body properties of a student, including gaze point and body posture. Machine learning algorithms are used to train classifiers which estimate time-varying attention levels of individual students. Human observers' estimation of attention level is used as a reference. The comparison of attention prediction accuracy of seven classifiers is done on a data set comprising 18 subjects. Our best person-independent three-level attention classifier achieved moderate accuracy of 0.753, comparable to results of other studies in the field of student engagement. The results indicate that Kinect-based attention monitoring system is able to predict both students' attention over time as well as average attention levels and could be applied as a tool for non-intrusive automated analytics of the learning process.
Context-aware recommender system (CARS) is a highly researched and implemented way of providing a personalized service that helps users to find their desired content. One of the remaining issues is how to decide which contextual information to acquire and how to incorporate it into CARS. While the relevant contextual information will improve the recommendations, the irrelevant contextual information could have a negative impact on the recommendation accuracy. By testing the independence between the contextual variable on the users' ratings for items, we can detect its relevance and impact on the feedback for the item consumed in that specific context. In this article, we propose several new theoretical concepts that should help deciding which information to use, as well as a methodology for detecting which contextual information contributes to explaining the variance in the ratings, based on statistical testing. The experiment was conducted on the real movie dataset that contains 12 different pieces of contextual information. We used two statistical tests with power analysis for the detection, and three contextualized matrix-factorization algorithms with slightly different reasoning for the prediction of ratings. The results showed a significant difference in the prediction of ratings in the context that was detected as relevant by our method, and the one that was detected as irrelevant, pointing to the importance of the power analysis and the benefits of the proposed method in the case of a small dataset. RESEARCH HIGHLIGHTS • We create a real context-aware movie-RS database. • We provide theoretical concepts for contextual-information selection. • We provide statistic-based method for relevant context detection. • Relevant contextual information enhances rating prediction. • Irrelevant contextual information degrades the rating prediction.
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