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Relevant bibliographies by topics / Explainable recommendation systems

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Journal articles

Academic literature on the topic 'Explainable recommendation systems'

Author: Grafiati

Published: 15 February 2025

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Journal articles on the topic "Explainable recommendation systems"

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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