Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28000
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Galke, Lukas
Mai, Florian
Vagliano, Iacopo
Scherp, Ansgar
Contact Email: ansgar.scherp@stir.ac.uk
Title: Multi-modal adversarial autoencoders for recommendations of citations and subject labels
Citation: Galke L, Mai F, Vagliano I & Scherp A (2018) Multi-modal adversarial autoencoders for recommendations of citations and subject labels. In: UMAP '18 Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. User Modeling, Adaptation and Personalization - UMAP 2018, Singapore, 08.07.2018-11.07.2018. New York: ACM, pp. 197-205. https://doi.org/10.1145/3209219.3209236
Issue Date: 31-Dec-2018
Conference Name: User Modeling, Adaptation and Personalization - UMAP 2018
Conference Dates: 2018-07-08 - 2018-07-11
Conference Location: Singapore
Abstract: We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.
Status: VoR - Version of Record
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