Caser+ and CosRec+: Closing the Gap Between CNNs and Attention Models in SRS

Sequential Recommender Systems (SRSs) have predominantly shifted toward neural-based models. Despite significant advances, Convolutional Neural Network (CNN)-based SRSs have been increasingly overshadowed by more powerful attention-based approaches. In this paper, we introduce a novel adaptation of two popular CNN-based SRSs, Caser and CosRec. We enhance their training by adjusting the convolution and pooling operations to process the entire input sequence simultaneously rather than focusing only on the most recent item. Experimental results show that these modified CNN-based models achieve improvements of up to +65% in NDCG@10 over their original versions. Code is available at https://github.com/antoniopurificato/recsys_conv_conf.