Taxonomy-Informed Latent Factor Models for Implicit Feedback

Andriy Mnih
Proceedings of KDD Cup 2011, PMLR 18:169-181, 2012.

Abstract

We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items that are sampled from the same distribution. Using the item relationship information from the provided taxonomy to constrain item representations results in improved predictive performance. Providing the model with features summarizing the user’s rating history as it relates to the item being ranked leads to further gains, producing the best single model result on Track 2.

Cite this Paper

BibTeX
@InProceedings{pmlr-v18-mnih12a, title = {Taxonomy-Informed Latent Factor Models for Implicit Feedback}, author = {Mnih, Andriy}, booktitle = {Proceedings of KDD Cup 2011}, pages = {169--181}, year = {2012}, editor = {Dror, Gideon and Koren, Yehuda and Weimer, Markus}, volume = {18}, series = {Proceedings of Machine Learning Research}, month = {21 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v18/mnih12a/mnih12a.pdf}, url = {https://proceedings.mlr.press/v18/mnih12a.html}, abstract = {We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items that are sampled from the same distribution. Using the item relationship information from the provided taxonomy to constrain item representations results in improved predictive performance. Providing the model with features summarizing the user’s rating history as it relates to the item being ranked leads to further gains, producing the best single model result on Track 2.} }
Endnote
%0 Conference Paper %T Taxonomy-Informed Latent Factor Models for Implicit Feedback %A Andriy Mnih %B Proceedings of KDD Cup 2011 %C Proceedings of Machine Learning Research %D 2012 %E Gideon Dror %E Yehuda Koren %E Markus Weimer %F pmlr-v18-mnih12a %I PMLR %P 169--181 %U https://proceedings.mlr.press/v18/mnih12a.html %V 18 %X We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items that are sampled from the same distribution. Using the item relationship information from the provided taxonomy to constrain item representations results in improved predictive performance. Providing the model with features summarizing the user’s rating history as it relates to the item being ranked leads to further gains, producing the best single model result on Track 2.
RIS
TY - CPAPER TI - Taxonomy-Informed Latent Factor Models for Implicit Feedback AU - Andriy Mnih BT - Proceedings of KDD Cup 2011 DA - 2012/06/01 ED - Gideon Dror ED - Yehuda Koren ED - Markus Weimer ID - pmlr-v18-mnih12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 18 SP - 169 EP - 181 L1 - http://proceedings.mlr.press/v18/mnih12a/mnih12a.pdf UR - https://proceedings.mlr.press/v18/mnih12a.html AB - We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup 2011, which required learning to discriminate between highly rated and unrated items from a large dataset of music ratings. We take the pairwise ranking route, training our models to rank the highly rated items above the unrated items that are sampled from the same distribution. Using the item relationship information from the provided taxonomy to constrain item representations results in improved predictive performance. Providing the model with features summarizing the user’s rating history as it relates to the item being ranked leads to further gains, producing the best single model result on Track 2. ER -
APA
Mnih, A.. (2012). Taxonomy-Informed Latent Factor Models for Implicit Feedback. Proceedings of KDD Cup 2011, in Proceedings of Machine Learning Research 18:169-181 Available from https://proceedings.mlr.press/v18/mnih12a.html.

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