Challenges and New Opportunities for Next-Generation Recommender Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 2441

Special Issue Editors

Department of Computer Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
Interests: recommender systems; natural language processing; multi-modal data analysis

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Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
Interests: recommender systems; data mining; personalized user modeling; streaming data analysis; graph learning-based recommendation; natural language processing for recommendations
Department of Information Technology and Management, College of Computing,Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: recommender systems; user modeling; technology-enhanced learning; fintech
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Special Issue Information

Dear Colleagues,

Recommender systems rely on users' explicit or/and latent interests to offer personalized suggestions of items automatically, aiming to help users cope with information overload. Despite its indispensable role in the areas of e-commerce, targeted advertising, intelligent medical assistants and online media, recommender systems are still facing many challenges which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of recommender systems, multi-stakeholder recommendation, data sparsity issues as well as misinformation detection. Fortunately, with the advancement of deep learning, multi-modal data have become better accessible for AI technology. The great success of language modelling techniques also inspires recommendation models to alleviate the aforementioned challenges through knowledge transmission and distillation from the pre-trained models.

This special issue solicits the latest and significant contributions on developing and applying efficient user modeling and advanced machine learning techniques for building next-generation recommender systems and particularly on data and model-driven intelligent recommender systems.

Topics of interest include but are not limited to, the following:

  • Cross-domain/source recommendation
  • Context-aware/group/multi-criteria/multi-objective recommender systems
  • Multi-modal recommendation
  • Explainanality/transparency in recommender systems
  • Fairness/bias in recommender systems
  • Efficient and scalable recommendation techniques
  • Pre-trained recommendation model
  • Self-supervised learning for recommendation
  • Data sparsity, cold start and long-tail issues in recommender systems
  • Knowledge transfer and distillation in recommender systems
  • Surveys, reviews and prospects on next-generation recommender systems

Dr. Peng Liu
Dr. Lemei Zhang
Dr. Yong Zheng
Guest Editors

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Keywords

  • recommender systems
  • collaborative filtering
  • cross-domain recommendation
  • multi-modality recommendation
  • NLP for recommendation
  • data sparsity and cold-start issue
  • explainable recommender systems
  • user modelling

Published Papers (2 papers)

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Research

19 pages, 1712 KiB  
Article
Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts
by Zhaoyu Shou, Yixin Chen, Hui Wen, Jinghua Liu, Jianwen Mo and Huibing Zhang
Electronics 2024, 13(7), 1272; https://doi.org/10.3390/electronics13071272 - 29 Mar 2024
Viewed by 407
Abstract
The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be [...] Read more.
The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods. Full article
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19 pages, 2348 KiB  
Article
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model
by Zhiyu Li, Yanfang Chen, Xuan Zhang and Xun Liang
Electronics 2023, 12(22), 4654; https://doi.org/10.3390/electronics12224654 - 15 Nov 2023
Cited by 1 | Viewed by 1494
Abstract
With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the [...] Read more.
With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks: book rating recommendation, user rating recommendation, and the book summary recommendation; it also explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers’ attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks. Full article
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