A Personalized Tag-Based Recommendation in Social Web Systems

TL;DR

This paper introduces a personalized tag-based recommendation system for social web platforms that enhances item suggestions by considering tag popularity, representativeness, and user-tag affinity, validated through a user experiment.

Contribution

It presents a novel tag similarity approach incorporating external factors and demonstrates its effectiveness with real user data from Del.icio.us.

Findings

Improved recommendation accuracy with external tag factors

Effective personalization based on user-tag affinity

Positive user feedback from diverse participants

Abstract

Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks.