{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-simple-convolutional-generative-network-for","title":"A Simple Convolutional Generative Network for Next Item Recommendation","arxiv_id":"1808.05163","date":"2018-08-15","proceeding":null,"authors":["Fajie Yuan","Alexandros Karatzoglou","Ioannis Arapakis","Joemon M. Jose","Xiangnan He"],"abstract":"Convolutional Neural Networks (CNNs) have been recently introduced in the\ndomain of session-based next item recommendation. An ordered collection of past\nitems the user has interacted with in a session (or sequence) are embedded into\na 2-dimensional latent matrix, and treated as an image. The convolution and\npooling operations are then applied to the mapped item embeddings. In this\npaper, we first examine the typical session-based CNN recommender and show that\nboth the generative model and network architecture are suboptimal when modeling\nlong-range dependencies in the item sequence. To address the issues, we\nintroduce a simple, but very effective generative model that is capable of\nlearning high-level representation from both short- and long-range item\ndependencies. The network architecture of the proposed model is formed of a\nstack of \\emph{holed} convolutional layers, which can efficiently increase the\nreceptive fields without relying on the pooling operation. Another contribution\nis the effective use of residual block structure in recommender systems, which\ncan ease the optimization for much deeper networks. The proposed generative\nmodel attains state-of-the-art accuracy with less training time in the next\nitem recommendation task. It accordingly can be used as a powerful\nrecommendation baseline to beat in future, especially when there are long\nsequences of user feedback.","url_abs":"http://arxiv.org/abs/1808.05163v4","url_pdf":"http://arxiv.org/pdf/1808.05163v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-simple-convolutional-generative-network-for","repo_url":"https://github.com/fajieyuan/WSDM2019-nextitnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"a-simple-convolutional-generative-network-for","repo_url":"https://github.com/microsoft/recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"a-simple-convolutional-generative-network-for","repo_url":"https://github.com/syiswell/NextItNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.05163","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}