Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users’ personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously i...

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users’ personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously i...

Collaborative €ltering (CF) is one of themost ecient ways for recommender systems. Typically, CF-based algorithms analyze users’ preferences and items’ aŠributes using one of two types of feedback: explicit feedback (e.g., ratings given to item by users, like/dislike) or implicit feedback (e.g., clicks, views, purchases). Explicit feedback is reliable but is extremely sparse; whereas implicit ...

How can we reliably infer web users’ interest and evaluate the content relevance when lacking active user interaction such as click behavior? In this paper, we investigate the relationship between mobile users’ implicit interest inferred from attention metrics, such as eye gaze or viewport time, and explicit interest expressed by users. We present the first quantitative gaze tracking study usin...

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains where personalization is highly valued. Such recommender systems typically base their suggestions on (1) collaborative data encoding which users like which items, and/or (2) content data describing ite...

Inferring user preferences over a set of items is an important problem that has found numerous applications. This work focuses on the scenario where the explicit feature representation of items is unavailable, a setup that is similar to collaborative filtering. In order to learn a user’s preferences from his/her response to only a small number of pairwise comparisons, we propose to leverage the...

In computer graphics and user interface design, selection problems are those that require the user to select a collection consisting of a small number of items from a much larger library. is dissertation explores selection problems in two diverse domains: large personal multimedia collections, containing items such as personal photographs or songs, and camera positions for 3D objects, where eac...