Session: User Studies

Date: Friday, October 24, 16:45-18:15

• A cross-cultural user evaluation of product recommender interfaces

  by Li Chen, Pearl Pu

  We present a cross-cultural user evaluation of an organization-based product recommender interface, by comparing it with the traditional list view. The results show that it performed significantly better, for all study participants, in improving on their competence perceptions, including perceived recommendation quality, perceived ease of use and perceived usefulness, and positively impacting users’ behavioral intentions such as intention to save effort in the next visit. Additionally, oriental users were observed reacting more significantly strongly to the organization interface regarding some subjective aspects, compared to western subjects. Through this user study, we also identified the dominating role of the recommender system’s decision-aiding competence in stimulating both oriental and western users’ return intention to an e-commerce website where the system is applied.

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• Personalized online document, image and video recommendation via commodity eye-tracking

  by Songhua Xu, Hao Jiang, Francis C.M. Lau

  We propose a new recommendation algorithm for online documents, images and videos, which is personalized. Our idea is to rely on the attention time of individual users captured through commodity eye-tracking as the essential clue. The prediction of user interest over a certain online item (a document, image or video) is based on the user’s attention time acquired using vision-based commodity eye-tracking during his previous reading, browsing or video watching sessions over the same type of online materials. After acquiring a user’s attention times over a collection of online materials, our algorithm can predict the user’s probable attention time over a new online item through data mining. Based on our proposed algorithm, we have developed a new online content recommender system for documents, images and videos. The recommendation results produced by our algorithm are evaluated by comparing with those manually labeled by users as well as by commercial search engines including Google (Web) Search, Google Image Search and YouTube.

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• Evaluation of an ontology-content based filtering method for a personalized newspaper

  by Veronica Maidel, Peretz Shoval, Bracha Shapira, Meirav Taieb-Maimon

  A new ontological-content-based method for ranking the relevancy of items in the electronic newspapers domain is proposed. The method is being implemented in ePaper, a personalized electronic newspaper research project. The content-based part of the filtering method of ePaper utilizes a hierarchical ontology of news items. The method considers common and “close” ontology concepts appearing in the user’s profile and in the item’s profile, measuring the hierarchical distance between concepts in the two profiles. Based on the number of common and related concepts, and their distances from each other, the filtering algorithm computes the similarity between items and users, and rank-orders the news items according to their relevancy to each user, thus providing a personalized newspaper.

  We have conducted evaluations of the filtering method, examining various parameters. A group of subjects, each having defined an initial content-based profile using the news ontology concepts, read news items from a certain electronic newspaper and expressed the relevancy of each item to them. In different runs of the algorithm on the same data, we changed several parameters of the algorithm, and compared the results with the users’ ratings. We discovered that the filtering method, which considers not only common concepts but also hierarchically related concepts, yields significantly better quality of filtering compared to using only common concepts. Moreover, we were able to find optimal values of similarity scores according to the hierarchical distance between related concepts.

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