The goal of the transparent Recommendation and Interest Modeling Application (RIMA) is to recommend items (e.g. tweets, Twitter users, publications, researchers, conferences) and leverage explanatory visualizations to explain the recommendations as well as support users in exploring, developing, and understanding their own interest models in order to provide more transparent and personalized recommendations.

These interest models are generated from users’ publications and tweets using Semantic Scholar and Twitter IDs provided by users. To address semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with Wikpedia/DBPedia as a knowledge base to generate semantically-enriched user interest models and leverages pre-trained transformer sentence encoders to represent user models and items and compute their similarities. 

RIMA follows a user-driven personalized explanation approach by providing explanations with different levels of detail and empowering users to steer the explanation process the way they see fit. Further, the application provides on-demand explanations, that is, the users can decide whether or not to see the explanation and they can also choose which level of explanation detail they want to see.

Project Features

We want to thank the team from Semantic Scholar for granting us access to their API. Without them, this project would not be possible. 

Team members

Publications

• Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Rawaa Alatrash, Clara Siepmann, Tannaz Vahidi
  Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System   Journal Article
  International Journal of Human–Computer Interaction, 2023; https://doi.org/10.1080/10447318.2023.2262797
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• Mouadh Guesmi, Mohamed Amine Chatti, Lamees Kadhim, Shoeb Joarder, Qurat Ul Ain
  Semantic Interest Modeling and Content-based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders   Journal Article Open Access
  Multimodal Technol. Interact. 2023, 7(9), 91; https://doi.org/10.3390/mti7090091
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• Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Clara Siepmann, Hoda Ghanbarzadeh, Rawaa Alatrash
  Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System   Inproceedings Open Access
  Information. 2023; 14(7):401. https://doi.org/10.3390/info14070401
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• Mouadh Guesmi, Clara Siepmann, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Rawaa Alatrash
  Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative Study   Inproceedings
  In Adjunct Proceedings of the 31th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’23 Adjunct), Limassol, Cyprus, June 2023.
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• Mouadh Guesmi, Mohamed Amine Chatti, Jaleh Ghorbani-Bavani, Shoeb Joarder, Qurat Ul Ain, Rawaa Alatrash
  What if Interactive Explanation in a Scientific Literature Recommender System   Inproceedings  
  In Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ’22).
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• Mohamed Amine Chatti, Mouadh Guesmi, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, Arham Muslim
  Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System  Inproceedings  
  In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Barcelona, Spain, July 2022.
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• Mouadh Guesmi, Mohamed Amine Chatti, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, Arham Muslim
  Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User Study   Inproceedings  
  In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22 Adjunct), Barcelona, Spain, July 2022.
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• Mouadh Guesmi, Mohamed Amine Chatti, Alptug Tayyar, Qurat Ul Ain, Shoeb Joarder
  Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach  Journal Article  
  In Multimodal Technologies and Interaction (MTI) journal, 2022.
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• Mouadh Guesmi, Mohamed Amine Chatti, Laura Vorgerd, Shoeb Joarder, Qurat Ul Ain, Thao Ngo, Shadi Zumor, Yiqi Sun, Fangzheng Ji, Arham Muslim
  Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details   inproceedings
  In Proceedings of the 8th joint workshop on interfaces and Human Decision Making for Recommender Systems (IntRS '21)
    
• Mouadh Guesmi, Mohamed Amine Chatti, Laura Vorgerd, Shoeb Joarder, Shadi Zumor, Yiqi Sun, Fangzheng Ji, Arham Muslim
  On-demand Personalized Explanation for Transparent Recommendation   Inproceedings
  In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21 Adjunct).
    
• Mohamed Amine Chatti, Fangzheng Ji, Mouadh Guesmi, Arham Muslim, Ravi Kumar Singh, and Shoeb Ahmed Joarder
  SIMT: A Semantic Interest Modeling Toolkit   Inproceedings
  In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21 Adjunct) ,
    
• Mouadh Guesmi, Mohamed Amine Chatti, Yiqi Sun, Shadi Zumor, Fangzheng Ji, Arham Muslim, Laura Vorgerd, and Shoeb Ahmed Joarder
  Open, Scrutable and Explainable Interest Models for Transparent Recommendation   Inproceedings
  In Companion Proceedings of the 26th International Conference on Intelligent User Interfaces ( IUI'21).