Our article on the impact of a tag recommendation system in a real-world folksonomy has been accepted for publication at ACM Trans. on Intelligent Systems and Technology! Hopefully it will soon be available. For the moment, you can check the abstract here or contact the authors for more details.
I am in the program committee of TRI 2015, the second second Workshop on Synergies between Multiagent Systems, Machine Learning and Complex Systems, which will be held at the International Joint Conference on Artificial Intelligence 2015 (IJCAI-15), July 25-27 2015, Buenos Aires, Argentina.
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. Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure ‘families’, and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared. J. Serrà and J. Ll. Arcos. An empirical evaluation of similarity measures for time series classification. Knowledge-Based Systems. In press. |
Archives
September 2016
|