Exploiting co-occurrence and information quality metrics to recommend tags in web 2.0 applications
Fabiano Muniz Belém, Eder Ferreira Martins, Jussara Marques Almeida, Marcos André Gonçalves, Gisele Lobo Pappa · 2010
This work addresses the task of recommending high quality tags by exploiting not only previously assigned tags, but also terms extracted from other textual features (e.g., title and description) associated with the target object.To estimate the quality of a candidate tag recommendation, we use several metrics related to both tag co-occurrence and information quality. We also propose a heuristic function to combine the metrics to produce a final ranking of the recommended tags. We evaluate our heuristic function in various scenarios, for three popular Web 2.0 applications. Our experimental results indicate that our heuristic function significantly outperforms two state-of-the-art tag recommendation algorithms.