Collaborative Filtering via Group-Structured Dictionary Learning

Collaborative Filtering via Group-Structured Dictionary Learning by Zoltan Szabo, Barnabas Poczos , and Andras Lorincz.

Abstract:

Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented method outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.

From the paper:

Novel advances on CF show that dictionary learning based approaches can be efficient for making predictions about users’ preferences [2]. The dictionary learning based approach assumes that (i) there is a latent, unstructured feature space (hidden representation/code) behind the users’ ratings, and (ii) a rating of an item is equal to the product of the item and the user’s feature.

Is a “preference” actually a form of subject identification?

I ask because the notion of a “real time” system is incompatible with users researching the proper canonical subject identifier and/or waiting for a response from an inter-departmental committee to agree on correct terminology.

Perhaps subject identification in some systems must be on the basis of “…latent, unstructured feature space[s]…” that are known (and disclosed) imperfectly at best.

Zoltán Szabó’s Home Page, numerous publications and the source code for this article.

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