issues, the thesis studies the following topics. 1. Identifying novel topics based on user interests. We introduce the method that extracts user interests from users ’ blog entries and measures similarity of users according to the taxonomy of items. We also introduce a new measure, score of novelty

Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can determine the interest of a user and use this information to make suggestions. The common solution, explicit ratings, where users tell

Abstract. This paper proposes a probabilistic method for classifying folksonomy users to specific domains and for identifying their specific interests to these domains. The proposed method uses a hierarchical probabilistic topic modeling approach that exploits tags to induce hierarchies of latent

advised. Finally we would like to thank every one who participated in our study and help us gather the data we needed to complete this project. User interest in content on a webpage is an important factor for mak-ing business decisions and improving user experience. Explicit ratings are often used as a

, independent predictors of interest in quitting included being married, having smoked for fewer years, not increasing the frequency of smoking over time, and having family members who do not smoke a waterpipe and disapprove of its use. Results indicate that a sizable percentage of waterpipe users express

Abstract: Rendering based on user interests is one of the hot topics in large-scale scene’s expressive rendering. In this paper, we present a novel semantic-based method for computing user interests, which uses semantic forest consisting of various semantic trees to describe the complex relevance

As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents

Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four ex-periments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51 % of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.

In this paper, we aim at detecting events of common user interests from huge volume of user-generated content. The degree of interest from common users in an event is evi-denced by a significant surge of event-related queries issued to search for documents (e.g., news articles, blog posts) rel

Abstract This paper presents a novel method and algorithm to measure the size of an open source project’s user base and the level of potential user interest that it generates. Previously unavailable download data at a daily resolution confirms hypothesized patterns related to release cycles