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Gating augmented capsule network for sequential recommendation
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AI
DOI：10.1016/j.knosys.2022.108817.png)
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Abstract
En 中文
Sequential recommendation has become a popular and indispensable component of various online services, which aims to predict the next interested item based on the sequence of a certain user. To deduce users' actual interests, sequential recommenders concentrate on analyzing the complex transition dependency from the user's recent action sequence. The key types of item transition patterns can be generally divided into item-level and factor-level. However, most existing works directly focus on a one-channel chain of interaction sequence, and only capture item co-occurrence patterns from item-level. They neglect the availability of transitions among items' latent attributes. Toward this end, we propose a Gating Augmented Capsule Network (GAC), which models both personalized item- and factor-level transitions in a fine-grained manner. Specifically, to distill user-specific information, we present a personalized gating module to replace the convolution operation of the traditional capsule network, so as to augment the links between the user and each item. Moreover, we design an item-routing component and a factor-routing component to build a two-channel routing module for capturing item- and factor-level interactions, respectively, while preserving the relative order of items in the action sequence. Extensive experiments on four public benchmarks demonstrate the effectiveness of our proposed GAC compared to several state-of-the-art baselines. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Capsule network
Gated mechanism
Sequential recommendation
Implicit feedback