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- W2887262017 abstract "Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously. Specifically, we leverage the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms. We then apply a greedy search algorithm that utilizes this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome dataset, which resulted in promising results: small and diverse sets of novel items." @default.
- W2887262017 created "2018-08-22" @default.
- W2887262017 creator A5047756027 @default.
- W2887262017 creator A5066073375 @default.
- W2887262017 date "2018-09-27" @default.
- W2887262017 modified "2023-09-24" @default.
- W2887262017 title "Generation meets recommendation" @default.
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- W2887262017 doi "https://doi.org/10.1145/3240323.3240357" @default.
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