The task of determining item similarity is a crucial one in a recommender system. This constitutes the base upon which the recommender system will work to determine which items are more likely to be enjoyed by a user, resulting in more user engagement. In this paper we tackle the problem of determining song similarity based solely on song metadata (such as the performer, and song title) and on tags contributed by users. We evaluate our approach under a series of different machine learning algorithms. We conclude that tf-idf achieves better results than Word2Vec to model the dataset to feature vectors. We also conclude that k-NN models have better performance than SVMs and Linear Regression for this problem.
@cite_1 use a set of boosted classifiers to map audio features onto tags collected from the Web. Due to the nature of their classifier, it uses the objective approach and, therefore, need the actual audio files, which we are not using. @cite_2 survey various music-similarity measures and concludes that measures derived from co-occurrence in personal music collections are the most useful ground truth metrics from those evaluated. @cite_3 define a song similarity measure based on the analysis of songs' timbres, and also evaluate their metric. @cite_4 proposes a matrix factorization method that works well for data with implicit feedback, such as song listening patterns.
@cite_4 @cite_1 @cite_3 @cite_2
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We observed that for multimedia data – especially music collaborative similarity measures perform much better than similarity measures derived from content-based sound features. Our observation is based on a large scale evaluation with >250,000,000 collaborative data points crawled from the web and >190,000 songs annotated with content-based sound feature sets. A song mentioned in a playlist is regarded as one collaborative data point. In this paper we present a novel approach to bridging the performance gap between collaborative and contentbased similarity measures. In the initial training phase a model vector for each song is computed, based on collaborative data. Each vector consists of 200 overlapping unlabelled 'genres' or song clusters. Instead of using explicit numerical voting, we use implicit user profile data as collaborative data source, which is, for example, available as purchase histories in many large scale ecommerce applications. After the training phase, we used support vector machines based on content-based sound features to predict the collaborative model vectors. These predicted model vectors are finally used to compute the similarity between songs. We show that combining collaborative and content-based similarity measures can help to overcome the new item problem in e-commerce applications that offer a collaborative similarity recommender as service to their customers. A novel homogeneity-based method for music structure analysis is proposed. The heart of the method is a similarity measure, derived from first principles, that is based on the matrix Elastic Net (EN) regularization and deals efficiently with highly correlated audio feature vectors. In particular, beat-synchronous mel-frequency cepstral coefficients, chroma features, and auditory temporal modulations model the audio signal. The EN induced similarity measure is employed to construct an affinity matrix, yielding a novel subspace clustering method referred to as Elastic Net subspace clustering (ENSC). The performance of the ENSC in structure analysis is assessed by conducting extensive experiments on the Beatles dataset. The experimental findings demonstrate the descriptive power of the EN-based affinity matrix over the affinity matrices employed in subspace clustering methods, attaining the state-of-the-art performance reported for the Beatles dataset. The rise of digital music distribution has provided users with unprecedented access to vast song catalogs. In order to help users cope with large collections, music information retrieval systems have been developed to automatically analyze, index, and recommend music based on a user's preferences or search criteria. This dissertation proposes machine learning approaches to content-based, query-by-example search, and investigates applications in music information retrieval. The proposed methods automatically infer and optimize content-based similarity, fuse heterogeneous feature modalities, efficiently index and search under the optimized distance metric, and finally, generate sequential playlists for a specified context or style. Robust evaluation procedures are proposed to counteract issues of subjectivity and lack of explicit ground truth in music similarity and playlist generation. Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically outperformed by collaborative filter methods. In this paper, we propose a method for optimizing content-based similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.