التفاصيل البيبلوغرافية
| العنوان: |
Local Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques |
| المؤلفون: |
Marta Caro-Martínez, Guillermo Jiménez-Díaz, Juan A. Recio-García |
| المصدر: |
International Journal of Interactive Multimedia and Artificial Intelligence, Vol 8, Iss 2, Pp 202-212 (2023) |
| بيانات النشر: |
Universidad Internacional de La Rioja (UNIR), 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Technology |
| مصطلحات موضوعية: |
artificial intelligence, graphs, link prediction, recommendation systems, Technology |
| الوصف: |
Explanations in recommender systems are a requirement to improve users’ trust and experience. Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles. However, this information is not available in black-box recommender systems that lack sufficient data transparency. This current work proposes a local model-agnostic, explanation-by-example method for recommender systems based on knowledge graphs to leverage this knowledge requirement. It only requires information about the interactions between users and items. Through the proper transformation of these knowledge graphs into item-based and user-based structures, link prediction techniques are applied to find similarities between the nodes and to identify explanatory items for the user’s recommendation. Experimental evaluation demonstrates that these knowledge graphs are more effective than classical content-based explanation approaches but have lower information requirements, making them more suitable for black-box recommender systems. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1989-1660 |
| Relation: |
https://www.ijimai.org/journal/bibcite/reference/3063; https://doaj.org/toc/1989-1660 |
| DOI: |
10.9781/ijimai.2021.12.001 |
| URL الوصول: |
https://doaj.org/article/c271a0ba370f4b298b380375f02c896c |
| رقم الانضمام: |
edsdoj.271a0ba370f4b298b380375f02c896c |
| قاعدة البيانات: |
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