A hybrid social-acoustic recommendation system for popular music

Donaldson, Justin

doi:10.1145/1297231.1297271

Cited by 22 publications

(11 citation statements)

References 23 publications

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“…There are several hybrid approaches combining acoustic-based and collaborative filtering music recommendation to improve the overall accuracy of predictions [Yoshii et al 2006;Li et al 2007;Tiemann and Pauws 2007;Donaldson 2007;Yoshii and Goto 2009]. Yoshii et al [2006] and Yoshii and Goto [2009] integrate both rating and music content information by using probabilistic models.…”

Section: Hybrid Music Recommendationmentioning

confidence: 99%

“…They apply ensemble learning methods to combine outputs of item-based collaborative filtering and acoustic-based recommendation. Donaldson [2007] exploits music co-occurring information in playlists and acoustic signals for a hybrid music recommender system by unifying spectral graph and acoustic feature vectors. All of these works use conventional collaborative filtering methods and only utilize limited kinds of information, without considering more sophisticated social media information.…”

Section: Hybrid Music Recommendationmentioning

confidence: 99%

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Using Rich Social Media Information for Music Recommendation via Hypergraph Model

Tan

Chen

et al. 2011

Social Media Modeling and Computing

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There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.

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Section: Hybrid Music Recommendationmentioning

confidence: 99%

Section: Hybrid Music Recommendationmentioning

confidence: 99%

Using Rich Social Media Information for Music Recommendation via Hypergraph Model

Tan

Chen

et al. 2011

Social Media Modeling and Computing

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“…With the availability of large amount of music/speech streams through a variety of sources and distribution channels, effectively and fast classifying music/speech tracks becomes an indispensable task in social music/speech websites and online music/speech communities [1,2]. Previous studies employed acoustic-based low-level feature [1], social media tag information [1], musicological analysis/expert opinions [2], or a combination of them [2] to represent and classify music/speech streams.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies employed acoustic-based low-level feature [1], social media tag information [1], musicological analysis/expert opinions [2], or a combination of them [2] to represent and classify music/speech streams. However, the accuracy of these methods is still rather far from satisfaction [2], due to the widely recognized 'semantic gap'.…”

Section: Introductionmentioning

confidence: 99%

Music/speech classification using high-level features derived from fmri brain imaging

Jiang

Zhang

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

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With the availability of large amount of audio tracks through a variety of sources and distribution channels, automatic music/speech classification becomes an indispensable tool in social audio websites and online audio communities. However, the accuracy of current acoustic-based low-level feature classification methods is still rather far from satisfaction. The discrepancy between the limited descriptive power of low-level features and the richness of high-level semantics perceived by the human brain has become the 'bottleneck' problem in audio signal analysis. In this paper, functional magnetic resonance imaging (fMRI) which monitors the human brain's response under the natural stimulus of music/speech listening is used as high-level features in the brain imaging space (BIS). We developed a computational framework to model the relationships between BIS features and low-level features in the training dataset with fMRI scans, predict BIS features of testing dataset without fMRI scans, and use the predicted BIS features for music/speech classification in the application stage. Experimental results demonstrated the significantly improved performance of music/speech classification via predicted BIS features than that via the original low-level features.

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“…There has been little work in the area of cross-domain music recommendations. The majority of music recommender systems focus solely on music domain and employ either collaborative recommendation techniques [1,2], content-based techniques that rely on domain-specific metadata [4], or a combination of both [23,10].…”

Section: Introductionmentioning

confidence: 99%

Matching information content with music

Kaminskas

2009

Proceedings of the Third ACM Conference on Recommender Systems

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This work addresses a particular kind of cross domain personalization task consisting of selecting simultaneously two items in two different domains and recommending them together because they fit the user preferences and also they fit well together. In particular, it is shown that given a personalized recommendation for points of interests (POIs), the user's selection of POIs can be influenced by enriching their presentation with music tracks that are matching the user's profile and also the POIs. This paper presents the results of an online, live-users, experiment where we evaluated alternative approaches for matching POIs and music, based on tagging and text matching.

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A hybrid social-acoustic recommendation system for popular music

Cited by 22 publications

References 23 publications

Using Rich Social Media Information for Music Recommendation via Hypergraph Model

Using Rich Social Media Information for Music Recommendation via Hypergraph Model

Music/speech classification using high-level features derived from fmri brain imaging

Matching information content with music

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