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Journal articles on the topic 'Explainable recommendation systems'

Author: Grafiati

Published: 15 February 2025

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1

Pasrija, Vatesh, and Supriya Pasrija. "Demystifying Recommendations: Transparency and Explainability in Recommendation Systems." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1376–83. http://dx.doi.org/10.22214/ijraset.2024.58541.

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Abstract: Recommendation algorithms are widely used, however many consumers want more clarity on why specific goods are recommended to them. The absence of explainability jeopardizes user trust, satisfaction, and potentially privacy. Improving transparency is difficult and involves the need for flexible interfaces, privacy protection, scalability, and customisation. Explainable recommendations provide substantial advantages such as enhancing relevance assessment, bolstering user interactions, facilitating system monitoring, and fostering accountability. Typical methods include giving summaries

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Lai, Kai-Huang, Zhe-Rui Yang, Pei-Yuan Lai, Chang-Dong Wang, Mohsen Guizani, and Min Chen. "Knowledge-Aware Explainable Reciprocal Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8636–44. http://dx.doi.org/10.1609/aaai.v38i8.28708.

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Reciprocal recommender systems (RRS) have been widely used in online platforms such as online dating and recruitment. They can simultaneously fulfill the needs of both parties involved in the recommendation process. Due to the inherent nature of the task, interaction data is relatively sparse compared to other recommendation tasks. Existing works mainly address this issue through content-based recommendation methods. However, these methods often implicitly model textual information from a unified perspective, making it challenging to capture the distinct intentions held by each party, which fu

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Leal, Fátima, Bruno Veloso, Benedita Malheiro, Juan C. Burguillo, Adriana E. Chis, and Horacio González-Vélez. "Stream-based explainable recommendations via blockchain profiling." Integrated Computer-Aided Engineering 29, no. 1 (2021): 105–21. http://dx.doi.org/10.3233/ica-210668.

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Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use case

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Yang, Mengyuan, Mengying Zhu, Yan Wang, et al. "Fine-Tuning Large Language Model Based Explainable Recommendation with Explainable Quality Reward." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9250–59. http://dx.doi.org/10.1609/aaai.v38i8.28777.

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Large language model-based explainable recommendation (LLM-based ER) systems can provide remarkable human-like explanations and have widely received attention from researchers. However, the original LLM-based ER systems face three low-quality problems in their generated explanations, i.e., lack of personalization, inconsistency, and questionable explanation data. To address these problems, we propose a novel LLM-based ER model denoted as LLM2ER to serve as a backbone and devise two innovative explainable quality reward models for fine-tuning such a backbone in a reinforcement learning paradigm

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Ai, Qingyao, Vahid Azizi, Xu Chen, and Yongfeng Zhang. "Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation." Algorithms 11, no. 9 (2018): 137. http://dx.doi.org/10.3390/a11090137.

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Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex mod

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Cho, Gyungah, Pyoung-seop Shim, and Jaekwang Kim. "Explainable B2B Recommender System for Potential Customer Prediction Using KGAT." Electronics 12, no. 17 (2023): 3536. http://dx.doi.org/10.3390/electronics12173536.

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The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems th

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Wang, Tongxuan, Xiaolong Zheng, Saike He, Zhu Zhang, and Desheng Dash Wu. "Learning user-item paths for explainable recommendation." IFAC-PapersOnLine 53, no. 5 (2020): 436–40. http://dx.doi.org/10.1016/j.ifacol.2021.04.119.

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Guesmi, Mouadh, Mohamed Amine Chatti, Shoeb Joarder, et al. "Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System." Information 14, no. 7 (2023): 401. http://dx.doi.org/10.3390/info14070401.

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Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referre

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Huang, Xiao, Pengjie Ren, Zhaochun Ren, et al. "Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020." ACM SIGIR Forum 54, no. 1 (2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.

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This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.

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Li, Lei, Yongfeng Zhang, and Li Chen. "Personalized Prompt Learning for Explainable Recommendation." ACM Transactions on Information Systems 41, no. 4 (2023): 1–26. http://dx.doi.org/10.1145/3580488.

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Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system’s ease of use, and gain users’ trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus

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Zhang, Yongfeng, and Xu Chen. "Explainable Recommendation: A Survey and New Perspectives." Foundations and Trends® in Information Retrieval 14, no. 1 (2020): 1–101. http://dx.doi.org/10.1561/1500000066.

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Zhu, Xingyu, Xiaona Xia, Yuheng Wu, and Wenxu Zhao. "Enhancing Explainable Recommendations: Integrating Reason Generation and Rating Prediction through Multi-Task Learning." Applied Sciences 14, no. 18 (2024): 8303. http://dx.doi.org/10.3390/app14188303.

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In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these “black-box” systems lack reasonable explanations for their recommendat

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B, Meenakshi,. "Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33859.

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The contemporary financial landscape necessitates loan recommendation systems that offer both accuracy and transparency. Conventional assessment methodologies often suffer from limitations in efficiency and transparency, leading to potential risks for both lenders and borrowers. This research proposes the development of a novel loan recommendation system that leverages the power of machine learning (ML) and Explainable Artificial Intelligence (XAI). The paper delves into the processes of data collection, preprocessing, model training, evaluation, and subsequent integration into a web applicati

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Doh, Ronky Francis, Conghua Zhou, John Kingsley Arthur, Isaac Tawiah, and Benjamin Doh. "A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings." Data 7, no. 7 (2022): 94. http://dx.doi.org/10.3390/data7070094.

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Recommender systems (RS) have been developed to make personalized suggestions and enrich users’ preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, prov

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Wang, Linlin, Zefeng Cai, Gerard De Melo, Zhu Cao, and Liang He. "Disentangled CVAEs with Contrastive Learning for Explainable Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13691–99. http://dx.doi.org/10.1609/aaai.v37i11.26604.

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Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a

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Gao, Jingyue, Xiting Wang, Yasha Wang, and Xing Xie. "Explainable Recommendation through Attentive Multi-View Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3622–29. http://dx.doi.org/10.1609/aaai.v33i01.33013622.

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Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e

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Wang, Xiang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. "Explainable Reasoning over Knowledge Graphs for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5329–36. http://dx.doi.org/10.1609/aaai.v33i01.33015329.

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Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holi

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Yang, Zuoxi, Shoubin Dong, and Jinlong Hu. "GFE: General Knowledge Enhanced Framework for Explainable Sequential Recommendation." Knowledge-Based Systems 230 (October 2021): 107375. http://dx.doi.org/10.1016/j.knosys.2021.107375.

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Syed, Muzamil Hussain, Tran Quoc Bao Huy, and Sun-Tae Chung. "Context-Aware Explainable Recommendation Based on Domain Knowledge Graph." Big Data and Cognitive Computing 6, no. 1 (2022): 11. http://dx.doi.org/10.3390/bdcc6010011.

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With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have received a great deal of research interest. Providing recommendations based on users’ natural language queries is an equally difficult undertaking. In this paper, we propose a novel, context-aware recommender system, based on domain KG, to respond to user-defined natural queries. The proposed recommender system consists of three stages. First, we g

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20

Jiang, Tianming, and Jiangfeng Zeng. "Time-Aware Explainable Recommendation via Updating Enabled Online Prediction." Entropy 24, no. 11 (2022): 1639. http://dx.doi.org/10.3390/e24111639.

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There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user–item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlook

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21

Takii, Kensuke, Brendan Flanagan, Huiyong Li, Yuanyuan Yang, Kento Koike, and Hiroaki Ogata. "Explainable eBook recommendation for extensive reading in K-12 EFL learning." Research and Practice in Technology Enhanced Learning 20 (September 10, 2024): 027. http://dx.doi.org/10.58459/rptel.2025.20027.

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An automatic recommendation system for learning materials in e-learning addresses the challenge of selecting appropriate materials amid information overload and varying self-directed learning (SDL) skills. Such systems can enhance learning by providing personalized recommendations. In Extensive Reading (ER) for English as a Foreign Language (EFL), recommending materials is crucial due to the paradox that learners with low SDL skills struggle to select suitable ER resources, despite ER’s potential to improve SDL. Additionally, determining the difficulty level of ER materials and assessing learn

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Liang, Qianqiao, Xiaolin Zheng, Yan Wang, and Mengying Zhu. "O3ERS: An explainable recommendation system with online learning, online recommendation, and online explanation." Information Sciences 562 (July 2021): 94–115. http://dx.doi.org/10.1016/j.ins.2020.12.070.

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Ankur Aggarwal. "Evolution of recommendation systems in the age of Generative AI." International Journal of Science and Research Archive 14, no. 1 (2025): 485–92. https://doi.org/10.30574/ijsra.2025.14.1.0061.

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This article examines the transformative evolution of recommendation systems in the era of Generative AI, exploring how these advanced technologies have revolutionized user experience and business outcomes across digital platforms. The article investigates the transition from traditional rule-based approaches to sophisticated model-based systems, highlighting the impact of deep learning technologies, explainable AI mechanisms, and multimodal integration. Through comprehensive analysis of recent developments, the article demonstrates how Generative AI has enhanced personalization capabilities,

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Tao, Shaohua, Runhe Qiu, Yuan Ping, and Hui Ma. "Multi-modal Knowledge-aware Reinforcement Learning Network for Explainable Recommendation." Knowledge-Based Systems 227 (September 2021): 107217. http://dx.doi.org/10.1016/j.knosys.2021.107217.

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Guo, Siyuan, Ying Wang, Hao Yuan, Zeyu Huang, Jianwei Chen, and Xin Wang. "TAERT: Triple-Attentional Explainable Recommendation with Temporal Convolutional Network." Information Sciences 567 (August 2021): 185–200. http://dx.doi.org/10.1016/j.ins.2021.03.034.

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Samir, Mina, Nada Sherief, and Walid Abdelmoez. "Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models." Computers 12, no. 7 (2023): 128. http://dx.doi.org/10.3390/computers12070128.

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Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-b

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Sopchoke, Sirawit, Ken-ichi Fukui, and Masayuki Numao. "Explainable and unexpectable recommendations using relational learning on multiple domains." Intelligent Data Analysis 24, no. 6 (2020): 1289–309. http://dx.doi.org/10.3233/ida-194729.

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In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with pro

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Priyanka Singla. "An Intelligent Job Recommendation System based on Semantic Embeddings and Machine Learning." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 520–42. https://doi.org/10.52783/jisem.v10i5s.681.

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To address the shortcomings in existing approaches of job recommendation systems, this paper proposes a novel machine-learning-based job recommendation system that performs bi-directional matching for dynamic and accurate recommendations. The proposed approach generates ideal job recommendations for a targeted Curriculum Vitae (CV) and vice versa. Unlike previous approaches, the proposed approach incorporates natural language processing (NLP) techniques to extract linguistic features such as Bag of Words (BoW), n-grams, TF-IDF, and Parts-of-Speech (PoS) tag and build a rich feature set. These

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Malikireddy, Sai Kiran Reddy. "Revolutionizing Product Recommendations with Generative AI: Context-Aware Personalization at Scale." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem40434.

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Generative Artificial Intelligence (GenAI) is poised to transform the product recommendation landscape by bridging the gap between user intent and personalized discovery. Traditional recommendation systems rely heavily on collaborative filtering, content-based algorithms, or hybrid models, often constrained by sparse data and limited contextual understanding. GenAI introduces a paradigm shift by leveraging advanced transformer-based architectures and multimodal embeddings to deliver highly contextual, dynamic, and explainable recommendations at scale. This paper explores the use of GenAI for p

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Zuo, Xianglin, Tianhao Jia, Xin He, Bo Yang, and Ying Wang. "Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information Networks." Entropy 24, no. 12 (2022): 1718. http://dx.doi.org/10.3390/e24121718.

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The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user–item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user–item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the

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Kim, Se Young, Dae Ho Kim, Min Ji Kim, Hyo Jin Ko, and Ok Ran Jeong. "XAI-Based Clinical Decision Support Systems: A Systematic Review." Applied Sciences 14, no. 15 (2024): 6638. http://dx.doi.org/10.3390/app14156638.

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With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliabl

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Lin, Yujie, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. "Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation." IEEE Transactions on Knowledge and Data Engineering 32, no. 8 (2020): 1502–16. http://dx.doi.org/10.1109/tkde.2019.2906190.

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Nyachama, Kerry. "Effectiveness of Recommender Systems in Knowledge Discovery." European Journal of Information and Knowledge Management 3, no. 1 (2024): 50–62. http://dx.doi.org/10.47941/ejikm.1753.

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Purpose: The general purpose of the study was to investigate the effectiveness of recommender systems in knowledge discovery. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statis

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Lin, Ching-Sheng, Chung-Nan Tsai, Shao-Tang Su, Jung-Sing Jwo, Cheng-Hsiung Lee, and Xin Wang. "Predictive Prompts with Joint Training of Large Language Models for Explainable Recommendation." Mathematics 11, no. 20 (2023): 4230. http://dx.doi.org/10.3390/math11204230.

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Large language models have recently gained popularity in various applications due to their ability to generate natural text for complex tasks. Recommendation systems, one of the frequently studied research topics, can be further improved using the capabilities of large language models to track and understand user behaviors and preferences. In this research, we aim to build reliable and transparent recommendation system by generating human-readable explanations to help users obtain better insights into the recommended items and gain more trust. We propose a learning scheme to jointly train the

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Yang, Zuoxi, and Shoubin Dong. "HAGERec: Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation." Knowledge-Based Systems 204 (September 2020): 106194. http://dx.doi.org/10.1016/j.knosys.2020.106194.

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Yang, Chao, Weixin Zhou, Zhiyu Wang, Bin Jiang, Dongsheng Li, and Huawei Shen. "Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence." Knowledge-Based Systems 213 (February 2021): 106687. http://dx.doi.org/10.1016/j.knosys.2020.106687.

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Liu, Peng, Lemei Zhang, and Jon Atle Gulla. "Dynamic attention-based explainable recommendation with textual and visual fusion." Information Processing & Management 57, no. 6 (2020): 102099. http://dx.doi.org/10.1016/j.ipm.2019.102099.

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Jing, Yanzhen, Guanghui Zhou, Chao Zhang, Fengtian Chang, Hairui Yan, and Zhongdong Xiao. "XMKR: Explainable manufacturing knowledge recommendation for collaborative design with graph embedding learning." Advanced Engineering Informatics 59 (January 2024): 102339. http://dx.doi.org/10.1016/j.aei.2023.102339.

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Caro-Martínez, Marta, Guillermo Jiménez-Díaz, and Juan A. Recio-García. "Conceptual Modeling of Explainable Recommender Systems: An Ontological Formalization to Guide Their Design and Development." Journal of Artificial Intelligence Research 71 (July 24, 2021): 557–89. http://dx.doi.org/10.1613/jair.1.12789.

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With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users’ trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful. Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation’s goal

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Zhang, Yongfeng, Xu Chen, Da Xu, and Tobias Schnabel. "Introduction to the Special Issue on Causal Inference for Recommender Systems." ACM Transactions on Recommender Systems 2, no. 2 (2024): 1–4. http://dx.doi.org/10.1145/3661465.

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A significant proportion of machine learning methodologies for recommendation systems are grounded in the fundamental principle of matching, utilizing perceptual and similarity-based learning approaches. These methods include both the extraction of features from data through representation learning and the derivation of similarity matching functions via neural function learning. While these models are important for recommendation systems, their foundational design philosophy primarily captures correlational signals within the data. Transitioning from correlation-based learning to causal learni

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Wang, Chao, Hengshu Zhu, Peng Wang, et al. "Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach." ACM Transactions on Information Systems 40, no. 4 (2022): 1–32. http://dx.doi.org/10.1145/3490476.

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As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the e

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Camastra, Francesco, Angelo Ciaramella, Giuseppe Salvi, Salvatore Sposato, and Antonino Staiano. "On the interpretability of fuzzy knowledge base systems." PeerJ Computer Science 10 (December 3, 2024): e2558. https://doi.org/10.7717/peerj-cs.2558.

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In recent years, fuzzy rule-based systems have been attracting great interest in interpretable and eXplainable Artificial Intelligence as ante-hoc methods. These systems represent knowledge that humans can easily understand, but since they are not interpretable per se, they must remain simple and understandable, and the rule base must have a compactness property. This article presents an algorithm for minimizing the fuzzy rule base, leveraging rough set theory and a greedy strategy. Reducing fuzzy rules simplifies the rule base, facilitating the construction of interpretable inference systems

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Chen, Chao, Dongsheng Li, Junchi Yan, Hanchi Huang, and Xiaokang Yang. "Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7011–19. http://dx.doi.org/10.1609/aaai.v35i8.16863.

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One-bit matrix completion is an important class of positive-unlabeled (PU) learning problems where the observations consist of only positive examples, e.g., in top-N recommender systems. For the first time, we show that 1-bit matrix completion can be formulated as the problem of recovering clean graph signals from noise-corrupted signals in hypergraphs. This makes it possible to enjoy recent advances in graph signal learning. Then, we propose the spectral graph matrix completion (SGMC) method, which can recover the underlying matrix in distributed systems by filtering the noisy data in the gra

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Younus, Yasir Mahmood. "An Explainable Content-Based Course Recommender Using Job Skills." AlKadhum Journal of Science 1, no. 2 (2023): 32–43. http://dx.doi.org/10.61710/akjs.v1i2.62.

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The large number of courses offered in universities and online studies made it difficult for students to choose the courses that suit their interests and career goals, which led students to lose many opportunities to be employed in the job they wanted. To keep pace with the rapid development of technology, and instead of relying on the job title as was previously done, the employers began to identify the skills required for a job. The competencies of the candidates are then examined and evaluated according to those requirements. Thus, it has become necessary for students to take courses that s

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Dai, Yiling, Kyosuke Takami, Brendan Flanagan, and Hiroaki Ogata. "Beyond recommendation acceptance: explanation’s learning effects in a math recommender system." Research and Practice in Technology Enhanced Learning 19 (September 12, 2023): 020. http://dx.doi.org/10.58459/rptel.2024.19020.

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Recommender systems can provide personalized advice on learning for individual students. Providing explanations of those recommendations are expected to increase the transparency and persuasiveness of the system, thus improve students’ adoption of the recommendation. Little research has explored the explanations’ practical effects on learning performance except for the acceptance of recommended learning activities. The recommendation explanations can improve the learning performance if the explanations are designed to contribute to relevant learning skills. This study conducted a comparative e

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Abu-Rasheed, Hasan, Christian Weber, Johannes Zenkert, Mareike Dornhöfer, and Madjid Fathi. "Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval." Informatics 9, no. 1 (2022): 6. http://dx.doi.org/10.3390/informatics9010006.

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In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the explainability of these systems was rarely investigated in such domain-specific environments. In addition to considering the domain requirements within an explainable intelligent IR, transferring the explainable IR algorithm to other domains remains an open-ended challenge. This is du

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Alhejaili, Abdullah, and Shaheen Fatima. "Expressive Latent Feature Modelling for Explainable Matrix Factorisation based Recommender Systems." ACM Transactions on Interactive Intelligent Systems, May 2, 2022. http://dx.doi.org/10.1145/3530299.

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The traditional matrix factorisation (MF) based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This paper introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable re

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Markchom, Thanet, Huizhi Liang, and James Ferryman. "Explainable Meta-Path Based Recommender Systems." ACM Transactions on Recommender Systems, September 28, 2023. http://dx.doi.org/10.1145/3625828.

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Meta-paths have been popularly used to provide explainability in recommendations. Although long/complicated meta-paths could represent complex user-item connectivity, they are not easy to interpret. This work tackles this problem by introducing a meta-path translation task. The objective is to translate a meta-path to its comparable explainable meta-paths that perform similarly in terms of recommendation but have higher explainability compared to the given one. We propose a definition of meta-path explainability to determine comparable explainable meta-paths and a meta-path grammar that allows

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"Ontology Reasoning Towards Sentimental Product Recommendations Explanations." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 4706–9. http://dx.doi.org/10.35940/ijrte.c6852.098319.

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In the last two decades various organizations like service industries, study communities, academic world and public industries are working intensely on sentiment analysis, to extract and analyze public views. The reviews given on the social websites, commercial websites, etc. enable customer to share their point of view. Explainable Recommendation algorithms help the user by providing explainable recommendations, which improves user satisfaction. Recently, many researchers proposed explainable recommendations. In this survey Firstly, various opinion-mining approaches are explored. Secondly, we

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Yu, Dianer, Qian Li, Xiangmeng Wang, Qing Li, and Guandong Xu. "Counterfactual Explainable Conversational Recommendation." IEEE Transactions on Knowledge and Data Engineering, 2023, 1–13. http://dx.doi.org/10.1109/tkde.2023.3322403.

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