Add to ORKGThe case presented in this paper describes an early prototype and next steps for developing a user-adaptive recommender system using semantic analysis and matching of user profiles and content. Machine learning methods optimize semantic analysis and matching based on implicit and explicit feedback of users. The constant interaction with users provides a valuable data source that is used to improve human-computer interaction and for adapting to specific user preferences. This can lead to, among others, higher accuracy and relevance in content matching, more intuitive graphical user interfaces, improved system performance, and better prioritization of tasks
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...
We explore a novel ontological approach to user profiling within recommender systems, working on the...
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...
International audienceIn this paper we present a methodology for learning user profiles from content...
This dissertation focuses on developing new machine learning models and algorithms for the task of l...
Recommendation systems are class of information filter applications whose main goal is to provide pe...
In this paper, we propose a technique that uses multimodal interactions of users to generate a more ...
Traditional approaches to recommender systems have often focused on the collaborative filtering prob...
Although machine learning is becoming commonly used in today's software, there has been little resea...
Personalization and recommendation systems are a solution to the problem of content overload, especi...
In this paper we propose to use radial layouts for representing the matching between the user’s inte...
Recently, there has been extensive interest in developing intelligent human-centered AI (artificial ...
Machine Learning seems to offer the solution to the central problem in recommender systems: Learning...
The motivation for this work comes from the need to obtain data for autonomous systems that rely hea...
Recommender systems support users in exploring items that would be interesting for them, building an...
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...
We explore a novel ontological approach to user profiling within recommender systems, working on the...
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...
International audienceIn this paper we present a methodology for learning user profiles from content...
This dissertation focuses on developing new machine learning models and algorithms for the task of l...
Recommendation systems are class of information filter applications whose main goal is to provide pe...
In this paper, we propose a technique that uses multimodal interactions of users to generate a more ...
Traditional approaches to recommender systems have often focused on the collaborative filtering prob...
Although machine learning is becoming commonly used in today's software, there has been little resea...
Personalization and recommendation systems are a solution to the problem of content overload, especi...
In this paper we propose to use radial layouts for representing the matching between the user’s inte...
Recently, there has been extensive interest in developing intelligent human-centered AI (artificial ...
Machine Learning seems to offer the solution to the central problem in recommender systems: Learning...
The motivation for this work comes from the need to obtain data for autonomous systems that rely hea...
Recommender systems support users in exploring items that would be interesting for them, building an...
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...
We explore a novel ontological approach to user profiling within recommender systems, working on the...
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing u...