Collaborative, Dynamic and Diversified User Profiling

User Profiling: Collaborative Filtering

Collaborative filtering is based on the assumption that “similar users have similar preferences”. In other words, by finding users that are similar to the active user and by examining their preferences, the recommender system can (i) predict the active user’s preferences for certain items and (ii) provide a ranked list of items which active user will most probably like. Collaborative filtering ...

Collaborative Evolution for User Profiling in Recommender Systems

Accurate user profiling is important for an online recommender system to provide proper personalized recommendations to its users. In many realworld scenarios, the user’s interests towards the items may change over time. Therefore, a dynamic and evolutionary user profile is needed. In this work, we come up with a novel evolutionary view of user’s profile by proposing a Collaborative Evolution (...

Dynamic Profiling in a Real-Time Collaborative Learning Environment

Fuzzy Induction in Dynamic User Profiling for Information Filtering

In this paper we investigate the role of the user profile in information filtering and we introduce a novel algorithm for learning the user profile based on user’s initial profile and on a queries’ interpretation using fuzzy generalization (Angryk and Petry 2003). Thousands of documents are usually retrieved by search engines for a given query during an information search on WWW. One way to pru...

Ontology-based Integration of Web Navigation for Dynamic User Profiling

The development of technology for handling information on a Big Data-scale is a buzzing topic of current research. Indeed, improved techniques for knowledge discovery are crucial for scientific and economic exploitation of large-scale raw data. In research collaboration with an industrial actor, we explore the applicability of ontology-based knowledge extraction and representation for today's b...