A Simple Convolutional Generative Network for Next Item Recommendation

Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M. and He, X. (2019) A Simple Convolutional Generative Network for Next Item Recommendation. In: Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, Australia, 11-15 Feb 2019, pp. 582-590. ISBN 9781450359405 (doi: 10.1145/3289600.3290975)

Abstract

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and YUAN, FAJIE
• Authors: Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., and He, X.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450359405
• Copyright Holders: Copyright © 2019 Association for Computing Machinery
• First Published: First published in WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
• Publisher Policy: Reproduced in accordance with the publisher copyright policy