Local model-agnostic explanations for black-box recommender systems using interaction graphs and link prediction techniques

• Marta Caro-Martínez ^[1] ; Guillermo Jiménez-Díaz ^[1] ; Juan A. Recio García ^[1]
  1. [1] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 8, Nº. 2, 2023, págs. 202-212
• Idioma: inglés
• DOI: 10.9781/ijimai.2021.12.001
• Enlaces
  • Texto completo (pdf)
• Resumen

  • 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.

• Referencias bibliográficas
  • C. C. Aggarwal, et al., Recommender systems. Springer, 2016.
  • J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, “Recommender systems survey,” Knowledge-based systems, vol. 46, pp. 109–132, 2013.
  • D. Jannach, M. Jugovac, I. Nunes, “Explanations and user control in recommender systems,” in Personalized Human-Computer Interaction, De...
  • N. Tintarev, J. Masthoff, “A survey of explanations in recommender systems,” in 2007 IEEE 23rd international conference on data engineering...
  • R. Sharma, S. Ray, “Explanations in recommender systems: an overview,” International Journal of Business Information Systems, vol. 23, no....
  • M. D. Ekstrand, J. T. Riedl, J. A. Konstan, et al., “Collaborative filtering recommender systems,” Foundations and Trends® in Human–Computer...
  • P. Lops, M. De Gemmis, G. Semeraro, “Content-based recommender systems: State of the art and trends,” in Recommender systems handbook, Springer,...
  • R. Sinha, K. Swearingen, “The role of transparency in recommender systems,” in CHI’02 extended abstracts on Human factors in computing systems,...
  • G. Friedrich, M. Zanker, “A taxonomy for generating explanations in recommender systems,” AI Magazine, vol. 32, no. 3, pp. 90–98, 2011.
  • J. L. Herlocker, J. A. Konstan, J. Riedl, “Explaining collaborative filtering recommendations,” in Proceedings of the 2000 ACM conference...
  • S. Ji, S. Pan, E. Cambria, P. Marttinen, S. Y. Philip, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE...
  • Q. Guo, F. Zhuang, C. Qin, H. Zhu, X. Xie, H. Xiong, Q. He, “A survey on knowledge graph- based recommender systems,” IEEE Transactions on...
  • M. T. Ribeiro, S. Singh, C. Guestrin, ““why should i trust you?” explaining the predictions of any classifier,” in Proceedings of the 22nd...
  • J. Singh, A. Anand, “Exs: Explainable search using local model agnostic interpretability,” in Proceedings of the Twelfth ACM International...
  • V. Arya, R. K. Bellamy, P.-Y. Chen, A. Dhurandhar, M. Hind, S. C. Hoffman, S. Houde, Q. V. Liao, R. Luss, A. Mojsilovic, et al., “One explanation...
  • M. T. Ribeiro, S. Singh, C. Guestrin, “Anchors: High- precision modelagnostic explanations,” in Proceedings of the AAAI Conference on Artificial...
  • M. Caro-Martinez, J. A. Recio-Garcia, G. Jimenez-Diaz, “An algorithm independent case-based explanation approach for recommender systems...
  • P. Wang, B. Xu, Y. Wu, X. Zhou, “Link prediction in social networks: the state-of-the-art,” Science China Information Sciences, vol. 58,...
  • D. Liben-Nowell, J. Kleinberg, “The link-prediction problem for social networks,” Journal of the American society for information science...
  • F. Ricci, L. Rokach, B. Shapira, “Introduction to recommender systems handbook,” in Recommender systems handbook, Springer, 2011, pp. 1–35....
  • F. Isinkaye, Y. Folajimi, B. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egyptian Informatics Journal, vol. 16,...
  • L. Sharma, A. Gera, “A survey of recommendation system: Research challenges,” International Journal of Engineering Trends and Technology...
  • J. B. Schafer, D. Frankowski, J. Herlocker, S. Sen, “Collaborative filtering recommender systems,” in The adaptive web, Springer, 2007, pp....
  • J. Bobadilla, A. Hernando, F. Ortega, J. Bernal, “A framework for collaborative filtering recommender systems,” Expert Systems with Applications,...
  • B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl, et al., “Item-based collaborative filtering recommendation algorithms.,” WWW, vol. 1,...
  • J. Bobadilla, F. Ortega, A. Hernando, J. Alcalá, “Improving collaborative filtering recommender system results and performance using genetic...
  • J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on...
  • M. De Gemmis, P. Lops, C. Musto, F. Narducci, G. Semeraro, “Semanticsaware content-based recommender systems,” in Recommender Systems Handbook,...
  • M. De Gemmis, P. Lops, G. Semeraro, P. Basile, “Integrating tags in a semantic content-based recommender,” in Proceedings of the 2008 ACM...
  • C. Musto, G. Semeraro, M. de Gemmis, P. Lops, “Learning word embeddings from wikipedia for content-based recommender systems,” in European...
  • J. Bobadilla, F. Ortega, A. Gutiérrez, S. Alonso, “Classification-based deep neural network architecture for collaborative filtering recommender...
  • H. Cordobés de la Calle, L. F. Chiroque, A. Fernández Anta, R. García, P. Morere, L. Ornella, F. Pérez, A. Santos, “Empirical comparison...
  • K. W. Darlington, “Designing for explanation in health care applications of expert systems,” Sage Open, vol. 1, no. 1, p. 2158244011408618,...
  • B. Walek, V. Fojtik, “A hybrid recommender system for recommending relevant movies using an expert system,” Expert Systems with Applications,...
  • N. Tintarev, “Explanations of recommendations,” in Proceedings of the 2007 ACM conference on Recommender systems, 2007, pp. 203–206, ACM.
  • Y. Zhang, X. Chen, “Explainable recommendation: A survey and new perspectives,” Foundations and Trends in Information Retrieval, vol. 14,...
  • I. Nunes, D. Jannach, “A systematic review and taxonomy of explanations in decision support and recommender systems,” User Modeling and UserAdapted...
  • M. Caro-Martinez, G. Jimenez-Diaz, J. A. Recio-Garcia, “A theoretical model of explanations in recommender systems,” ICCBR 2018, p. 52, 2018....
  • A. Papadimitriou, P. Symeonidis, Y. Manolopoulos, “A generalized taxonomy of explanations styles for traditional and social recommender systems,”...
  • F. Gedikli, D. Jannach, M. Ge, “How should I explain? a comparison of different explanation types for recommender systems,” International...
  • P. Kouki, J. Schaffer, J. Pujara, J. O’Donovan, L. Getoor, “Personalized explanations for hybrid recommender systems,” in Proceedings of...
  • L. Quijano-Sanchez, C. Sauer, J. A. Recio-Garcia, B. Diaz-Agudo, “Make it personal: a social explanation system applied to group recommendations,”...
  • I. Andjelkovic, D. Parra, J. O’Donovan, “Moodplay: Interactive music recommendation based on artists’ mood similarity,” International Journal...
  • X. Wang, X. He, F. Feng, L. Nie, T.-S. Chua, “Tem: Tree-enhanced embedding model for explainable recommendation,” in Proceedings of the 2018...
  • J. Jorro-Aragoneses, M. Caro-Martinez, J. A. Recio-Garcia, B. DiazAgudo, G. Jimenez-Diaz, “Personalized case-based explanation of matrix...
  • F. Sørmo, J. Cassens, A. Aamodt, “Explanation in case- based reasoning– perspectives and goals,” Artificial Intelligence Review, vol. 24,...
  • J. Bennett, S. Lanning, et al., “The netflix prize,” in Proceedings of KDD cup and workshop, vol. 2007, 2007, p. 35, New York, NY, USA.
  • M. Nickel, K. Murphy, V. Tresp, E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proceedings of the IEEE,...
  • L. Qiao, L. Zhang, S. Chen, D. Shen, “Data-driven graph construction and graph learning: A review,” Neurocomputing, vol. 312, pp. 336–351,...
  • Q. Wang, Z. Mao, B. Wang, L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Transactions on Knowledge and...
  • H. Paulheim, “Knowledge graph refinement: A survey of approaches and evaluation methods,” Semantic web, vol. 8, no. 3, pp. 489–508, 2017.
  • Y. Lin, X. Han, R. Xie, Z. Liu, M. Sun, “Knowledge representation learning: A quantitative review,” 2018, https://arxiv.org/abs/1812.10901....
  • Y. Chong, Y. Ding, Q. Yan, S. Pan, “Graph-based semi- supervised learning: A review,” Neurocomputing, 2020.
  • B. Furht, Handbook of social network technologies and applications. Springer Science & Business Media, 2010.
  • L. Lü, T. Zhou, “Link prediction in complex networks: A survey,” Physica A: statistical mechanics and its applications, vol. 390, no. 6,...
  • N. N. Daud, S. H. Ab Hamid, M. Saadoon, F. Sahran, N. B. Anuar, “Applications of link prediction in social networks: A review,” Journal of...
  • N. Chiluka, N. Andrade, J. Pouwelse, “A link prediction approach to recommendations in large-scale user- generated content systems,” in European...
  • Z. Huang, W. Chung, T.-H. Ong, H. Chen, “A graph- based recommender system for digital library,” in Proceedings of the 2nd ACM/IEEE-CS joint...
  • H. Chen, X. Li, Z. Huang, “Link prediction approach to collaborative filtering,” in Digital Libraries, 2005. JCDL’05. Proceedings of the...
  • H. Wang, F. Zhang, X. Xie, M. Guo, “Dkn: Deep knowledge-aware network for news recommendation,” in Proceedings of the 2018 world wide web...
  • A. Shahmohammadi, E. Khadangi, A. Bagheri, “Presenting new collaborative link prediction methods for activity recommendation in facebook,”...
  • T. Zhou, J. Ren, M. Medo, Y.-C. Zhang, “Bipartite network projection and personal recommendation,” Physical Review E, vol. 76, no. 4, p....
  • I. Tiddi, et al., “Foundations of explainable knowledge- enabled systems,” Knowledge Graphs for eXplainable Artificial Intelligence: Foundations,...
  • F. Lecue, “On the role of knowledge graphs in explainable ai,” Semantic Web, no. Preprint, pp. 1–11, 2019.
  • N. Barbieri, F. Bonchi, G. Manco, “Who to follow and why: link prediction with explanations,” in 20th ACM SIGKDD International Conference...
  • Y. Xian, Z. Fu, S. Muthukrishnan, G. De Melo, Y. Zhang, “Reinforcement knowledge graph reasoning for explainable recommendation,” in Proceedings...
  • X. Wang, D. Wang, C. Xu, X. He, Y. Cao, T.-S. Chua, “Explainable reasoning over knowledge graphs for recommendation,” in Proceedings of the...
  • M. Caro-Martinez, G. Jimenez-Diaz, “Similar users or similar items? comparing similarity-based approaches for recommender systems in online...
  • G. Jimenez-Diaz, P. P. Gómez-Martín, M. A. Gómez- Martín, A. A. Sánchez-Ruiz, “Similarity metrics from social network analysis for content...
  • G. Jimenez-Diaz, P. P. G. Martín, M. A. G. Martín, A. A. Sánchez-Ruiz, “Similarity metrics from social network analysis for content recommender...
  • S. Dooms, A. Bellogín, T. D. Pessemier, L. Martens, “A framework for dataset benchmarking and its application to a new movie rating dataset,”...