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View all- Wang YZhang DWulamu A(2021)A Multitask Learning Model with Multiperspective Attention and Its Application in RecommendationComputational Intelligence and Neuroscience10.1155/2021/85502702021Online publication date: 15-Oct-2021
Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
Pages 498 - 502
Abstract
In the one-class recommendation problem, it’s required to make recommendations basing on users’ implicit feedback, which is inferred from their action and inaction. Existing works obtain representations of users and items by encoding positive and negative interactions observed from training data. However, these efforts assume that all positive signals from implicit feedback reflect a fixed preference intensity, which is not realistic. Consequently, representations learned with these methods usually fail to capture informative entity features that reflect various preference intensities.
In this paper, we propose a multi-tasking framework taking various preference intensities of each signal from implicit feedback into consideration. Representations of entities are required to satisfy the objective of each subtask simultaneously, making them more robust and generalizable. Furthermore, we incorporate attentive graph convolutional layers to explore high-order relationships in the user-item bipartite graph and dynamically capture the latent tendencies of users toward the items they interact with. Experimental results show that our method performs better than state-of-the-art methods by a large margin on three large-scale real-world benchmark datasets.
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- Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
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September 2020
796 pages
ISBN:9781450375832
DOI:10.1145/3383313
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Published: 22 September 2020
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View all- Wang YZhang DWulamu A(2021)A Multitask Learning Model with Multiperspective Attention and Its Application in RecommendationComputational Intelligence and Neuroscience10.1155/2021/85502702021Online publication date: 15-Oct-2021
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