Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Autor:	Tang, Jiaxi, Wang, Ke
Rok vydání:	2018
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics. Comment: Accepted at WSDM 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.07426 Zobrazit plný text záznamu View this record from Arxiv