Nouvelle approche de recommandation personnalisee dans les folksonomies basee sur le profil des utilisateurs

TL;DR

This paper introduces a personalized recommendation approach in folksonomies that considers user profiles as a new dimension, grouping users with similar interests to improve tag and resource suggestions.

Contribution

It proposes a novel method to incorporate user profiles into folksonomy-based recommendations by using quadratic concepts to group similar users and interests.

Findings

Encouraging precision results in experiments

Effective user grouping based on profiles

Improved personalization in recommendations

Abstract

In folksonomies, users use to share objects (movies, books, bookmarks, etc.) by annotating them with a set of tags of their own choice. With the rise of the Web 2.0 age, users become the core of the system since they are both the contributors and the creators of the information. Yet, each user has its own profile and its own ideas making thereby the strength as well as the weakness of folksonomies. Indeed, it would be helpful to take account of users' profile when suggesting a list of tags and resources or even a list of friends, in order to make a personal recommandation, instead of suggesting the more used tags and resources in the folksonomy. In this paper, we consider users' profile as a new dimension of a folksonomy classically composed of three dimensions <users, tags, ressources> and we propose an approach to group users with equivalent profiles and equivalent interests as quadratic concepts. Then, we use such structures to propose our personalized recommendation system of users, tags and resources according to each user's profile. Carried out experiments on two real-world datasets, i.e., MovieLens and BookCrossing highlight encouraging results in terms of precision as well as a good social evaluation.