Tikhonov and Sobolev regularisers compared to user-based KNN collaborative filtering

Collaborative filtering is used to predict the preference or rating of a user for a certain item. Collaborative filtering is based on the notion that similar users rate similarly. A lot of research is done on how to improve this algorithm, mostly with deep learning. A less investigated field for recommender systems is graph signal processing....

The Performance of Total Variation Regularizer for User Collaborative Filtering

Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques...

CHAINels: Journal

A group of three students worked a couple of months at CHAINels for their computer science bachelor project. In these months a recommendation algorithm was designed and implemented in CHAINels. The recommendation algorithm recommends posts to a company and those posts are shown in the Journal which was also made during this project. In this...

From Talents to Team: Matching supply and demand with algorithms

The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations...

Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations

Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in multiple domains when there is knowledge about the user’s preferences for items in at least one of the domains and when there is knowledge about...

RACE:GP – a Generic Approach to Automatically Creating and Evaluating Hybrid Recommender Systems

In recent years, recommender systems have become a fundamental part of our online experience. Users rely on such systems in situations with many potential choices, such as watching a movie on a streaming service, reading a blog post, or listening to a song. Traditionally, these systems use techniques such as collaborative filtering and content...