Contextual music information retrieval and recommendation: State of the art and challenges

Kaminskas, Marius; Ricci, Francesco

doi:10.1016/j.cosrev.2012.04.002

Cited by 161 publications

(93 citation statements)

References 40 publications

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“…For 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 example, none of these four music pieces (13)(14)(15)(16) are similar with the other music pieces (1-12) in Table 8. Thirdly, some slight differences in styles and genres of music pieces are also shown by the learned embeddings, which shows that the learned embeddings by MEM can effectively capture the accurate features of the corresponding music pieces.…”

Section: Illustrations Of Selected Music Pieces' Embeddingsmentioning

confidence: 99%

“…Especially, music pieces are not neutral items but carriers of emotions and thoughts, and listening to music is a typical context-dependent behavior because people usually prefer different kinds of music under different contexts [16]. For instance, people generally prefer energetic music pieces with fast rhythm when doing exercises, and enjoy smoothing music pieces when resting.…”

Section: Introductionmentioning

confidence: 99%

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Learning to embed music and metadata for context-aware music recommendation

et al. 2017

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Contextual factors greatly influence users' musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users' historical listening records. Specifically, the proposed approach first learns music pieces' embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user's preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

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Section: Illustrations Of Selected Music Pieces' Embeddingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning to embed music and metadata for context-aware music recommendation

et al. 2017

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“…Abowd et al [16] distinguish among primary and secondary context: the first can be directly measured, while the second not and needs to be derived from other types of contextual information. Kaminskas and Ricci [17] reviewed literature about contextual music retrieval. They distinguish among environmental, user-related and multimedia context: the first refers to information about the location of the user, the current time, weather, temperature, etc.…”

Section: A Context Representationmentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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“…current location, activity, or any other contextual condition that might influence the user's perception or evaluation of music [1]. In response to these observations, in recent years a new research topic of contextual music retrieval and recommendation has emerged [9]. In this work we are addressing a particular type of context-aware music recommendationrecommending music for places of interest (POIs).…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based music retrieval for places of interest

Kaminskas

Fernández-Tobías

Ricci

et al. 2012

Proceedings of the Second International ACM Workshop on Music Information Retrieval With User-Centered and Multimodal Strategie

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In this paper we address a particular recommendation task: retrieving musicians suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology linking items from different domains. Graphbased algorithms are used for ranking and filtering items in a target domain (music) with respect to their relatedness to an input item in a source domain (POIs). By conducting user studies we found that users appreciate and judge more valuable the suggestions generated by the proposed approach when a novel weight spreading activation algorithm is used to compute the matching between musicians and POIs. Moreover, users perceive compositions of the suggested musicians as suited for the POIs.

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Contextual music information retrieval and recommendation: State of the art and challenges

Cited by 161 publications

References 40 publications

Learning to embed music and metadata for context-aware music recommendation

Learning to embed music and metadata for context-aware music recommendation

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Knowledge-based music retrieval for places of interest

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