A User Study on Explanations with Different Levels of Detail in Recommender Systems
Resumo
Explanations are a useful feature to improve the transparency and reliability of recommender systems. However, the amount of information presented to the user regarding a recommendation is an important parameter that is not usually considered. On the one hand, large explanations may carry more information than the users would like, which could be a potential case of the information overload problem. Similarly, if short explanations are provided, some users may find insufficient information to make decisions. In this context, our main objective is to analyze perceptions of users that were presented to explanations with varying level of detail. In our validation process, we conducted user experiments and identified a correlation between users reporting that the ability to choose the level of detail in the explanation aids in finding a recommendation that aligns with their interests, and their subsequent acceptance of the recommendation.
Palavras-chave:
Recommender Systems, Explanations, Text Summarization
Referências
Charu C Aggarwal 2016. Recommender systems. Vol. 1. Springer, New York.
Ramesh Baral, XiaoLong Zhu, S. S. Iyengar, and Tao Li. 2018. ReEL: Review Aware Explanation of Location Recommendation. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 23––32. https://doi.org/10.1145/3209219.3209237
Mohamed Amine Chatti, Mouadh Guesmi, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, and Arham Muslim. 2022. Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 254–264. https://doi.org/10.1145/3503252.3531304
Martijn Millecamp, Cristina Conati, and Katrien Verbert. 2022. "Knowing me, knowing you": personalized explanations for a music recommender system. User Model. User Adapt. Interact. 32, 1-2 (2022), 215–252. https://doi.org/10.1007/s11257-021-09304-9
Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To Explain or Not to Explain: The Effects of Personal Characteristics When Explaining Music Recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 397––407. https://doi.org/10.1145/3301275.3302313
Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Justifying Recommendations through Aspect-Based Sentiment Analysis of Users Reviews. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (Larnaca, Cyprus) (UMAP ’19). Association for Computing Machinery, New York, NY, USA, 4––12. https://doi.org/10.1145/3320435.3320457
Cataldo Musto, Gaetano Rossiello, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2019. Combining Text Summarization and Aspect-Based Sentiment Analysis of Users’ Reviews to Justify Recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 383–387. https://doi.org/10.1145/3298689.3347024
Arpit Rana and Derek Bridge. 2018. Explanations That Are Intrinsic to Recommendations. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 187–195. https://doi.org/10.1145/3209219.3209230
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems: Introduction and Challenges. Springer US, Boston, MA, 1–34. https://doi.org/10.1007/978-1-4899-7637-6_1
Luan Soares de Souza and Marcelo Garcia Manzato. 2022. Aspect-Based Summarization: An Approach With Different Levels of Details to Explain Recommendations. In Proceedings of the Brazilian Symposium on Multimedia and the Web (Curitiba, Brazil) (WebMedia ’22). Association for Computing Machinery, New York, NY, USA, 202–210. https://doi.org/10.1145/3539637.3557002
Nava Tintarev and Judith Masthoff. 2015. Explaining Recommendations: Design and Evaluation. Springer US, Boston, MA, 353–382. https://doi.org/10.1007/978-1-4899-7637-6_10
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101. https://doi.org/10.1561/1500000066
Ramesh Baral, XiaoLong Zhu, S. S. Iyengar, and Tao Li. 2018. ReEL: Review Aware Explanation of Location Recommendation. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 23––32. https://doi.org/10.1145/3209219.3209237
Mohamed Amine Chatti, Mouadh Guesmi, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, and Arham Muslim. 2022. Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 254–264. https://doi.org/10.1145/3503252.3531304
Martijn Millecamp, Cristina Conati, and Katrien Verbert. 2022. "Knowing me, knowing you": personalized explanations for a music recommender system. User Model. User Adapt. Interact. 32, 1-2 (2022), 215–252. https://doi.org/10.1007/s11257-021-09304-9
Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To Explain or Not to Explain: The Effects of Personal Characteristics When Explaining Music Recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 397––407. https://doi.org/10.1145/3301275.3302313
Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Justifying Recommendations through Aspect-Based Sentiment Analysis of Users Reviews. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (Larnaca, Cyprus) (UMAP ’19). Association for Computing Machinery, New York, NY, USA, 4––12. https://doi.org/10.1145/3320435.3320457
Cataldo Musto, Gaetano Rossiello, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2019. Combining Text Summarization and Aspect-Based Sentiment Analysis of Users’ Reviews to Justify Recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 383–387. https://doi.org/10.1145/3298689.3347024
Arpit Rana and Derek Bridge. 2018. Explanations That Are Intrinsic to Recommendations. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 187–195. https://doi.org/10.1145/3209219.3209230
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems: Introduction and Challenges. Springer US, Boston, MA, 1–34. https://doi.org/10.1007/978-1-4899-7637-6_1
Luan Soares de Souza and Marcelo Garcia Manzato. 2022. Aspect-Based Summarization: An Approach With Different Levels of Details to Explain Recommendations. In Proceedings of the Brazilian Symposium on Multimedia and the Web (Curitiba, Brazil) (WebMedia ’22). Association for Computing Machinery, New York, NY, USA, 202–210. https://doi.org/10.1145/3539637.3557002
Nava Tintarev and Judith Masthoff. 2015. Explaining Recommendations: Design and Evaluation. Springer US, Boston, MA, 353–382. https://doi.org/10.1007/978-1-4899-7637-6_10
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101. https://doi.org/10.1561/1500000066
Publicado
23/10/2023
Como Citar
DE SOUZA, Luan Soares; MANZATO, Marcelo Garcia.
A User Study on Explanations with Different Levels of Detail in Recommender Systems. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 208–212.
