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.
| Original language | English |
|---|---|
| Article number | 4432655 |
| Pages (from-to) | 435-447 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Audio, Speech and Language Processing |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2008 Feb |
| Externally published | Yes |
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Keywords
- Aspect model
- Hybrid collaborative and content-based recommendation
- Incremental training
- Music recommender system
- Probabilistic generative model
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Acoustics and Ultrasonics
Cite this
An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. / Yoshii, Kazuyoshi; Goto, Masataka; Komatani, Kazunori; Ogata, Tetsuya; Okuno, Hiroshi G.
In: IEEE Transactions on Audio, Speech and Language Processing, Vol. 16, No. 2, 4432655, 02.2008, p. 435-447.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model
AU - Yoshii, Kazuyoshi
AU - Goto, Masataka
AU - Komatani, Kazunori
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2008/2
Y1 - 2008/2
N2 - 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.
AB - 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.
KW - Aspect model
KW - Hybrid collaborative and content-based recommendation
KW - Incremental training
KW - Music recommender system
KW - Probabilistic generative model
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UR - http://www.scopus.com/inward/citedby.url?scp=39649112098&partnerID=8YFLogxK
U2 - 10.1109/TASL.2007.911503
DO - 10.1109/TASL.2007.911503
M3 - Article
VL - 16
SP - 435
EP - 447
JO - IEEE Transactions on Speech and Audio Processing
JF - IEEE Transactions on Speech and Audio Processing
SN - 1558-7916
IS - 2
M1 - 4432655
ER -