Marginal Constrained Latent Variable Modeling for Ranking and Collaborative Filtering
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چکیده
Latent or hidden variable based statistical methods constitute a set of valuable techniques for modeling co-occurrence data, as they can effectively trade-off the simplicity of assuming independence among the features with the computational intractability of modeling a full joint distribution. Such models however are primarily “explanatory” rather than “predictive”. In particular, the “closed world” assumption implicit in existing latent variable based models of co-occurrence data limit there usefulness in settings where new entities and/or features are added to the domain. In this paper we parameterize the latent variable model so that they can be applied to such dynamic or online scenarios. Applications include predicting buyers for a new product release(buyer-product co-occurrence), identifying items that would be bought with the new release (itemitem co-occurrence), predicting social associates of a person new to a community and also predicting the Pageranks of uncrawled pages.
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