User geospatial context for music recommendation in microblogs

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of do-ing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recom-mendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses seman-tic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collec-tion, the “Million Musical Tweets Dataset ” of listing events extracted from microblogs. Overall, we find that modeling listeners ’ location via Gaussian mixture models and comput-ing similarities from these outperforms both cultural user models and collaborative filtering. Categories and Subject Descriptors Information systems [Information retrieval]: Music rec-ommendation; Human-centered computing [Collaborative and social computing]: Social medi

MatRec: Matrix Factorization for Highly Skewed Dataset

Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature. Although recommender systems have received great success, it is well known for highly skewed datasets, engineers and researchers need to adjust their methods to tackle the specific problem to yield good results. Inability to deal with highly skewed dataset usually generates hard computational problems for big data clusters and unsatisfactory results for customers. In this paper, we propose a new algorithm solving the problem in the framework of matrix factorization. We model the data skewness factors in the theoretic modeling of the approach with easy to interpret and easy to implement formulas. We prove in experiments our method generates comparably favorite results with popular recommender system algorithms such as Learning to Rank , Alternating Least Squares and Deep Matrix Factorization

Learning to tag from open vocabulary labels

Abstract. Most approaches to classifying media content assume a fixed, closed vocabulary of labels. In contrast, we advocate machine learning approaches which take advantage of the millions of free-form tags obtainable via online crowd-sourcing platforms and social tagging websites. The use of such open vocabularies presents learning challenges due to typographical errors, synonymy, and a potentially unbounded set of tag labels. In this work, we present a new approach that organizes these noisy tags into well-behaved semantic classes using topic modeling, and learn to predict tags accurately using a mixture of topic classes. This method can utilize an arbitrary open vocabulary of tags, reduces training time by 94% compared to learning from these tags directly, and achieves comparable performance for classification and superior performance for retrieval. We also demonstrate that on open vocabulary tasks, human evaluations are essential for measuring the true performance of tag classifiers, which traditional evaluation methods will consistently underestimate. We focus on the domain of tagging music clips, and demonstrate our results using data collected with a human computation game called TagATune

Rupture fragile des pieces minces et methodes des equations integrales

SIGLECNRS-CDST / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc