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AI for Recommendation Systems and Their Applications
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AI for Recommendation Systems and Their Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 19 March 2027 | Viewed by 848

Special Issue Editors

Dr. Shoujin Wang

E-Mail Website
Guest Editor
Data Science and a Data Science Innovative Application Driver, University of Technology Sydney, Sydney, Australia
Interests: data science; machine learning; behavior modelling and recommender systems; misinformation mitigation and trustworthy and generative AI and their innovative applications in addressing real-world challenges
Special Issues, Collections and Topics in MDPI journals
Dr. Longxiang Shi

E-Mail Website
Guest Editor
College of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China
Interests: recommender systems; reinforcement learning

Special Issue Information

Dear Colleagues,

Recommendation systems play a critical role in modern intelligent services, driving personalization across varied domains such as e-commerce, social media, healthcare, education, finance, and smart cities. With the rapid advancement of artificial intelligence (AI), especially in deep learning, graph learning, reinforcement learning, and large language models, recommendation systems have undergone significant transformation in terms of modeling capability, scalability, and adaptability. 

This Special Issue will bring together cutting-edge research and practical advancements that explore how AI techniques can be leveraged to design, enhance, and deploy next-generation recommendation systems. We particularly encourage submissions that address real-world challenges, theoretical foundations, and innovative applications of AI-driven recommendation technologies.

Dr. Shoujin Wang
Dr. Longxiang Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-powered recommendation models and algorithms
  • multi-interest, multi-intent, and user behavior modeling
  • sequential, session-based, and context-aware recommendation
  • graph neural networks and knowledge-aware recommendation
  • reinforcement learning and bandit-based recommendation systems
  • contrastive, self-supervised, and representation learning for recommendation
  • large language models and foundation models for recommendation
  • explainable, trustworthy, and fair recommendation systems
  • privacy-preserving and robust recommendation methods
  • cross-domain, multi-modal, and multi-task recommendation
  • recommendation systems for emerging applications (e.g., healthcare, education, smart manufacturing, cultural heritage, scientific discovery)
  • industrial applications and real-world deployment of AI-based recommendation systems
  • recommender systems for various applications in real-world domains, e.g., healthcare, education, agriculture, etc.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 2592 KB  
Article
Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph
by Shunping Niu, Kuo Chi, Ting Su, Yongqin Yang and Jiabao Gao
AI 2026, 7(6), 215; https://doi.org/10.3390/ai7060215 - 11 Jun 2026
Viewed by 65
Abstract
Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, [...] Read more.
Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, relation-aware knowledge-graph aggregation. The hypergraph branch learns high-order item co-occurrence representations, which are aggregated into initial user vectors and then refined through user similarity propagation. On the item side, user-conditioned relation attention aggregates one-hop KG neighbors to produce semantic item representations. User and item representations are fused by an MLP scorer, and a lightweight popularity-aware post-scoring adjustment can optionally be applied to moderate head-item dominance. Experiments on MovieLens-1M, Last.FM and Book-Crossing show strong performance among the compared baselines in AUC, ACC, and Recall@K. Full article
(This article belongs to the Special Issue AI for Recommendation Systems and Their Applications)
31 pages, 1179 KB  
Article
MoHyNet: Enhancing Session-Based Recommendations via Hypergraph Motifs and Contrastive Learning
by Junkun Hong, Zhipeng Zhou, Shiyu Song, Peng Lan and Junfeng Man
AI 2026, 7(6), 197; https://doi.org/10.3390/ai7060197 - 28 May 2026
Viewed by 347
Abstract
Session-based recommendation seeks to deliver personalized suggestions by decoding transient interaction sequences generated by anonymous users. Although graph neural networks have advanced this field by modeling pairwise item transitions, they fundamentally struggle to capture the complex, high-order dependencies inherent in real-world user behavior [...] Read more.
Session-based recommendation seeks to deliver personalized suggestions by decoding transient interaction sequences generated by anonymous users. Although graph neural networks have advanced this field by modeling pairwise item transitions, they fundamentally struggle to capture the complex, high-order dependencies inherent in real-world user behavior modeling. Consequently, while hypergraphs offer a natural mathematical solution for representing these multi-item relationships, existing approaches frequently overlook the localized structural semantics necessary to ground these abstract relations in physical browsing logic. To address these limitations, we introduce MoHyNet, a novel motif-guided hypergraph framework explicitly designed to capture both inter- and intra-session dependencies. By extracting localized hypergraph motifs, MoHyNet effectively decodes the recurring topological sub-structures and latent intentions behind anonymous interactions. Rather than treating hypergraphs merely as static representations of item co-occurrence, our approach utilizes these motifs as dynamic semantic filters to extract stable behavioral signatures from pseudo-sequential noise. To complement this intra-session modeling, we construct an augmented line graph that maps multi-hop dependencies across different sessions, employing neighborhood-aware convolutions to aggregate global collaborative context. A dual-view contrastive learning optimization is subsequently integrated to semantically align these intra-session structural signatures with the inter-session global context, ensuring a robust and holistic understanding of user intent. Extensive empirical evaluations on three real-world e-commerce datasets demonstrate that MoHyNet consistently outperforms state-of-the-art methods in session-based recommendation performance. Full article
(This article belongs to the Special Issue AI for Recommendation Systems and Their Applications)
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