Loading…

A sequence recommendation method based on external reinforcement and position separation

Sequential Recommendation systems play a crucial role in predicting users’ preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model’s ability to ful...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2024-09, Vol.80 (14), p.20378-20399
Main Authors: Wu, Wenya, Wang, Guangjin, Liang, Xiufang, Zhu, Yingzheng, Duan, Huajuan, Liu, Peiyu, Lu, Ran
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sequential Recommendation systems play a crucial role in predicting users’ preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model’s ability to fully utilize relevant external properties. To alleviate these limitations, we design a recommendation model that incorporates Position encoding and external reinforcement (Item -Provider), named DPSRec. Specifically, we design an Embed Encoding layer, in order to distinguish the Position Embedding of previous sequence models, we combine the time variability with the position encoding with extrapolation property, and encode the item and item provider sequences accordingly. Meanwhile, to avoid the noise that the initial item embeddings might cause with Position Encoding, we calculate the position Encoding separately from the item embedding. In addition, we design a Cross Propagation layer to capture implicit higher-order dependencies between item sequences. Extensive experiments on three real-world datasets demonstrate that the proposed model generally outperforms the baselines by about 1–12.5%. Our source code will be published after the paper is published.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06260-0