In this work, we present a tag recommendation system and evaluate it in the context of an online platform for audio clip sharing. By exploiting domain-specific knowledge, the system we present is able to classify an audio clip among a number of predefined audio classes and to produce specific tag recommendations for the different classes. We perform an in-depth user-based evaluation of the recommendation method along with two baselines and a former version that we described in previous work. This user-based evaluation is further complemented with a prediction-based evaluation following standard information retrieval methodologies. Results show that the proposed tag recommendation method brings a statistically significant improvement over the previous method and the baselines. In addition, we report a number of findings based on the detailed analysis of user feedback provided during the evaluation process. The considered methods, when applied to real-world collaborative tagging systems, should serve the purpose of consolidating the tagging vocabulary and improving the quality of content annotations.
F. Font, J. Serrà, and X. Serra. Class-based tag recommendation and user-based evaluation in online audio clip sharing. Knowledge-Based Systems 67: 131-142. Sep 2014.