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Recommendation Generation Justified For Information Access Assistance Service (IAAS) : Study Of Architectural Approaches


Affiliations
1 Department of Informatic, Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso
2 Department of Informatic, Université Norbert Zongo, Koudougou, Burkina Faso
3 IRIT, Toulouse, France
 

Recommendation systems only provide more specific recommendations to users. They do not consider giving a justification for the recommendation. However, the justification for the recommendation allows the user to make the decision whether or not to accept the recommendation. It also improves user satisfaction and the relevance of the recommended item. However, the IAAS recommendation system that uses advisories to make recommendations does not provide a justification for the recommendations. That is why in this article, our task consists for helping IAAS users to justify their recommendations. For this, we conducted a related work on architectures and approaches for justifying recommendations in order to identify an architecture and approach suitable for the context of IAAS. From the analysis in this article, we note that neither of these approaches uses the notices (IAAS mechanism) to justify their recommendations. Therefore, existing architectures cannot be used in the context of IAAS. That is why, we have developed a new IAAS architecture that deals separately with item filtration and justification extraction that accompanied the item during recommendation generation (Figure 7). And we have improved the reviews by adding users’ reviews on the items. The user’s notices include the Documentary Unit (DU), the user Group (G), the Justification (J) and the weight (a); noted A=(DU,G,J,a).

Keywords

IAAS, justification of recommendations, weight of comments, relevance of recommendations, justification of recommendation architecture for IAAS.
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  • Cataldo M., Alain D.S., Christoph T., Amon R., and Giovanni S. 2021. Exploring the Effects of Natural Language Justifications in Food Recommender Systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ‘21), June 21–25, 2021, Utrecht, Netherlands. ACM, New York, NY, USA, 16 pages. https://doi.org/10.1145/3450613.3456827

  • Kabore, K. , Peninou, A., Sié, O. , Sèdes, F. Implementing The Information Access Assistant Service (IAAS) For An Evaluation. Int. J. Internet Technology and Secured Transactions , Vol. 6, No. 1, 2015 (2015)

  • Kabore, K. , Sié, O. , Sèdes, F. Information Access Assistant Service (IAAS). In The 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013), IEEE UK/RI Computer Chapter, London, UK, December 9-12, (2013).

  • Kiswendsida Kisito Kaboré : Système d‘aide pour l‘accès non supervisé aux unités documentaire. Thèse de doctorat du l‘Université de Ouaga 1 Pr Joseph KI-ZERBO, Janvier 2018.

  • Panagiotis S., Alexandros N., and Yannis M. Providing Justifications in Recommender Systems. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 38, NO. 6, NOVEMBER 2008 pp. 1262—1272 https://ieeexplore.ieee.org/abstract/document/4648950

  • T. Murali and S. Kasif, ―Extracting conserved gene expression motifs from gene expression data,‖ in Proc. Pacific Symp. Biocomputing Conf., 2003, vol. 8, pp. 77–88.

  • Cataldo M., Gaetano R., Marco de G., Pasquale L., Giovanni S. Natural Language Justifications for Recommender Systems Exploiting Text Summarization and Sentiment Analysis . DDC@AI*IA , volume 2495 of CEUR Workshop Proceedings, pp 63-73. CEUR-WS.org, (2019). http://ceurws.org/Vol-2495/paper8.pdf

  • Or B. and Courtenay C. 2017. Explanation and Justification in Machine Learning: A Survey. In IJCAI-17 Workshop on Explainable AI (XAI), VOL. 8, NO.1. pp 1-13

  • Radev, D.R., Jing, H., Sty, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manage. 40(6), 919–938 (2004)

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS. pp. 3111–3119 (2013)

  • Jianmo N., Jiacheng L., and Julian M.. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 188–197.

  • Arpit R. and Derek B. 2017. Explanation Chains: Recommendation by Explanation. RecSys ‘17 Poster Proceedings, Como, Italy, August 27–31, 2017, 2 pages.

  • Mann, W.C. and Sandra A. T. (1988). "Rhetorical Structure Theory: Toward a functional theory of text organization." Text 8 (3): 243-281.

  • Jacob D., Ming-Wei C., Kenton L., and Kristina T. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. pp. 2324—2335.

  • Yongfeng Z., Guokun L., Min Z., Yi Z., Yiqun L., and Shaoping M. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval , pp. 83-92. ACM

  • Or B., Kathleen M. Generating Justifications of Machine Learning Predictions. 1st International Workshop on Data-to-text 2015 http://www.cs.columbia.edu/~orb/papers/d2t_2015.pdf

  • Mohamed Hédi Maâloul. Approche hybride pour le résumé automatique de textes. Application à la langue arabe. PhD thesis, pp 17- 43, 18 décembre 2012. https://tel.archivesouvertes.fr/tel00756111/file/These.pdf

  • Mustafa B. and Raymond J. M.. 2005. Explaining recommendations: Satisfaction vs. promotion. In Beyond Personalization Workshop, IUI, Vol. 5. 153. pp 1,pp 7.

  • Roza Lémdani. Système Hybride d'Adaptation dans les Systèmes de Recommandation. Thèse de Doctorat de l‘Université Paris-Saclay préparée à CentraleSupelec. pp 23- 33. 11 juillet 2016. https://www.theses.fr/2016SACLC050.pdf

  • Kyelem Y., Kabore K.K., Bassole D. (2022) Hybrid Approach to Cross-Platform Mobile Interface Development for IAAS. In: Shakya S., Bestak R., Palanisamy R., Kamel K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 68. Springer, Singapore. https://doi.org/10.1007/978-981-16-1866-6_16

  • N. Park, A. Kan, C. Faloutsos and X. L. Dong, "J-Recs: Principled and Scalable Recommendation Justification," 2020 IEEE International Conference on Data Mining (ICDM), 2020, pp. 1208-1213,

  • doi: 10.1109/ICDM50108.2020.00151.

  • Panagiotis Symeonidis, Alexandros Nanopoulos and Yannis Manolopoulos ; MoviExplain: A Recommender System with Explanations RecSys '09: Proceedings of the third ACM conference on

  • Recommender systems October 2009 pp 317–320 https://doi.org/10.1145/1639714.1639777

  • W. K. Cheng, A. A. Ileladewa and T. B. Tan, "A Personalized Recommendation Framework for Social Internet of Things (SIoT)," 2019 International Conference on Green and Human Information Technology (ICGHIT), 2019, pp. 24-29, doi:10.1109/ICGHIT.2019.00013.

  • Mauro N., Hu Z.F., Ardissono L. (2021) Service-Oriented Justification of Recommender System Suggestions. In: Ardito C. et al. (eds) Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science, vol 12934. Springer, Cham. https://doi.org/10.1007/978-3-030-85613-7_23


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  • Recommendation Generation Justified For Information Access Assistance Service (IAAS) : Study Of Architectural Approaches

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Authors

Kyelem Yacouba
Department of Informatic, Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso
Kabore Kiswendsida Kisito
Department of Informatic, Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso
Ouedraogo Tounwendyam Frédéric
Department of Informatic, Université Norbert Zongo, Koudougou, Burkina Faso
Sèdes Florence
IRIT, Toulouse, France

Abstract


Recommendation systems only provide more specific recommendations to users. They do not consider giving a justification for the recommendation. However, the justification for the recommendation allows the user to make the decision whether or not to accept the recommendation. It also improves user satisfaction and the relevance of the recommended item. However, the IAAS recommendation system that uses advisories to make recommendations does not provide a justification for the recommendations. That is why in this article, our task consists for helping IAAS users to justify their recommendations. For this, we conducted a related work on architectures and approaches for justifying recommendations in order to identify an architecture and approach suitable for the context of IAAS. From the analysis in this article, we note that neither of these approaches uses the notices (IAAS mechanism) to justify their recommendations. Therefore, existing architectures cannot be used in the context of IAAS. That is why, we have developed a new IAAS architecture that deals separately with item filtration and justification extraction that accompanied the item during recommendation generation (Figure 7). And we have improved the reviews by adding users’ reviews on the items. The user’s notices include the Documentary Unit (DU), the user Group (G), the Justification (J) and the weight (a); noted A=(DU,G,J,a).

Keywords


IAAS, justification of recommendations, weight of comments, relevance of recommendations, justification of recommendation architecture for IAAS.

References