Latent factor models have been proved to be the state of the art for the Collaborative Filtering approach in a Recommender System. However, latent factors obtained with mathematical methods applied to the user-item matrix can be hardly interpreted by humans. In this paper we exploit Topic Models applied to textual data associated with items to find explanations for latent factors. Based on the MovieLens dataset and textual data about movies collected from Freebase we run a user study with over hundred participants to develop a reference dataset for evaluating different strategies towards more interpretable and portable latent factor models.

Rossetti, M., Stella, F., Zanker, M. (2013). Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems. In 24th International Workshop on Database and Expert Systems Applications (pp.162-167) [10.1109/DEXA.2013.26].

Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems

ROSSETTI, MARCO;STELLA, FABIO ANTONIO;
2013

Abstract

Latent factor models have been proved to be the state of the art for the Collaborative Filtering approach in a Recommender System. However, latent factors obtained with mathematical methods applied to the user-item matrix can be hardly interpreted by humans. In this paper we exploit Topic Models applied to textual data associated with items to find explanations for latent factors. Based on the MovieLens dataset and textual data about movies collected from Freebase we run a user study with over hundred participants to develop a reference dataset for evaluating different strategies towards more interpretable and portable latent factor models.
paper
recommender systems; topic models
English
International Workshop on Recommender Systems meet Databases
2013
24th International Workshop on Database and Expert Systems Applications
9780769550701
2013
162
167
6621365
none
Rossetti, M., Stella, F., Zanker, M. (2013). Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems. In 24th International Workshop on Database and Expert Systems Applications (pp.162-167) [10.1109/DEXA.2013.26].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/45080
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