Modelling local and global dependencies for next-item recommendations

Nan Wang, Shoujin Wang, Yan Wang*, Quan Z. Sheng, Mehmet Orgun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

14 Citations (Scopus)


Session-based recommender systems (SBRSs) aim at predicting the next item by modelling the complex dependencies within and across sessions. Most of the existing SBRSs make recommendations only based on local dependencies (i.e., the dependencies between items within a session), while ignoring global dependencies (i.e., the dependencies across multiple sessions), leading to information loss and thus reducing the recommendation accuracy. Moreover, they are usually not able to recommend cold-start items effectively due to their limited session information. To alleviate these shortcomings of SBRSs, we propose a novel heterogeneous mixed graph learning (HMGL) framework to effectively learn both local and global dependencies for next-item recommendations. The HMGL framework mainly contains a heterogeneous mixed graph (HMG) construction module and an HMG learning module. The HMG construction module map both the session information and the item attribute information into a unified graph to connect items within and across sessions. The HMG learning module learns a unified representation for each item by simultaneously modelling the local and global dependencies over the HMG. The learned representation is then used for next-item recommendations. Results of extensive experiments on real-world datasets show the superiority of HMGL framework over the start-of-the-art methods in terms of recommendation accuracy.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2020
Subtitle of host publication21st International Conference Amsterdam, The Netherlands, October 20–24, 2020 Proceedings, Part II
EditorsZhisheng Huang, Wouter Beek, Hua Wang, Rui Zhou, Yanchun Zhang
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages16
ISBN (Electronic)9783030620080
ISBN (Print)9783030620073
Publication statusPublished - 2020
Event21st International Conference on Web Information Systems Engineering, WISE 2020 - Amsterdam, Netherlands
Duration: 20 Oct 202024 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12343 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Web Information Systems Engineering, WISE 2020


  • Session-based recommendations
  • Heterogeneous mixed graph learning
  • Next-item recommendation
  • Graph neural network


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