%0 Generic %1 tang2018personalized %A Tang, Jiaxi %A Wang, Ke %D 2018 %K caser diss preprocessing recommendation session %T Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding %U http://arxiv.org/abs/1809.07426 %X 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 (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.

@misc{tang2018personalized, abstract = {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.}, added-at = {2023-02-07T14:24:42.000+0100}, author = {Tang, Jiaxi and Wang, Ke}, biburl = {https://www.bibsonomy.org/bibtex/28f0b1f943bf5c1c7804a51c88c141b92/e.fischer}, description = {[1809.07426] Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding}, interhash = {4e4193027b9875bf84b114b74447e674}, intrahash = {8f0b1f943bf5c1c7804a51c88c141b92}, keywords = {caser diss preprocessing recommendation session}, note = {cite arxiv:1809.07426Comment: Accepted at WSDM 2018}, timestamp = {2023-02-07T14:24:42.000+0100}, title = {Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding}, url = {http://arxiv.org/abs/1809.07426}, year = 2018 }