Personalization of user profiles for content-based music retrieval based on relevance feedback

Hoashi, Keiichiro; Matsumoto, Kazunori; Inoue, Nozomu

doi:10.1145/957039.957040

Cited by 34 publications

(32 citation statements)

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“…As taste may change over time, recommender systems should be able to adapt accordingly. Hoashi et al (2003) train a Tree Vector Quantizer (TreeQ) to discriminate songs that a user likes and dislikes on a small initial training set. The TreeQ is used to generate reference histograms from the bin assignments of all songs belonging to one class.…”

Section: User-adaptive Recommendationmentioning

confidence: 99%

Adaptive music retrieval–a state of the art

Stober

Nürnberger

2012

Multimed Tools Appl

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With the development of more and more sophisticated Music Information Retrieval (MIR) approaches, aspects of adaptivity are becoming an increasingly important research topic. Even though, adaptive techniques have already found their way into MIR systems and contribute to robustness or user satisfaction they are not always identified as such. This paper attempts a structured view on the last decade of MIR research from the perspective of adaptivity in order to increase awareness and promote the application and further development of adaptive techniques. To this end, different approaches from a wide range of application areas that share the common aspect of adaptivity are identified and systematically categorized.

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Section: User-adaptive Recommendationmentioning

confidence: 99%

Adaptive music retrieval–a state of the art

Stober

Nürnberger

2012

Multimed Tools Appl

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“…The recommendation process can be content-based, i.e., using features of the music liked by the user to predict what the target user may like [4], or collaborativebased, which finds users with similar music preferences and recommend to the target user items liked by these users [7]. However, most of the available music recommender systems suggest music regardless of the contextual conditions which can be important to predict the user's preferences at a particular moment.…”

Section: Introductionmentioning

confidence: 99%

Location-Adapted Music Recommendation Using Tags

Kaminskas

Ricci

2011

Lecture Notes in Computer Science

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Abstract. Context-aware music recommender systems are capable to suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context aware recommendation task -selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags attached by a users' population to both music and POIs. Moreover, we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. In order to test our hypothesis, i.e., that the users will reckon that a music track suits a POI when this track is selected by our approach, we have designed a live user experiment where subjects are repeatedly presented with POIs and a selection of music tracks, some of them matching the presented POI and some not. The results of the experiment show that there is a strong overlap between the users' selections and the best matching music that is recommended by the system for a POI.

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“…Content-based methods [9]- [12] recommend musical pieces similar to the users' favorites in terms of musical properties. This results in a large variety of artists; i.e., various pieces are recommended even when they have not been rated.…”

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confidence: 99%

“…Unfortunately, reliable methods of doing this have not been established. For example, although Hoashi et al [9] tried to model user preferences, their method was only verified using an artificial database where 12 subjects were asked to give rating scores. Logan [10] did not use real rating scores and instead took a set of songs in a CD album as a particular user's set of favorites.…”

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confidence: 99%

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An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

Yoshii

Goto

Komatani

et al. 2008

IEEE Trans. Audio Speech Lang. Process.

122

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Abstract-This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user's favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added.Index Terms-Aspect model, hybrid collaborative and contentbased recommendation, incremental training, music recommender system, probabilistic generative model.

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Personalization of user profiles for content-based music retrieval based on relevance feedback

Cited by 34 publications

References 0 publications

Adaptive music retrieval–a state of the art

Adaptive music retrieval–a state of the art

Location-Adapted Music Recommendation Using Tags

An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

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