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Trustworthy Recommendations
@ IEEE DSAA 2022

  October 13-16, 2022  
  Hybrid (Virtual and Onsite)
  Shenzhen, China  

About the Special Session

Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention in everyday real life. Recommender systems, as one of the most popular applications of AI, have already become an indispensable means for helping web users identify the most relevant information/services in the era of information overload. The applications of such systems are multi-faceted, including targeted advertising, intelligent financial assistant, and e-commerce, and are bringing immense convenience to people’s daily lives. However, despite rapid advances in recommendation, the increasing public awareness of the trustworthiness of recommender systems has now introduced higher expectations on relevant research.

The Special session on “Data Science and Artificial Intelligence Enabled Trustworthy Recommendations” aims to engage with active researchers from recommendation communities and deliver the state-of-the-art research insights into the core challenges in the algorithmic trustworthiness. Firstly, the unprecedentedly growing heterogeneity in real-world recommendation data has been challenging the adaptivity of contemporary algorithms to various settings, e.g., interest drift of users, cold-start users/items, highly interaction sparsity, and multimodal content. Secondly, trustworthy recommendation approaches should also be robust, secure, interpretable, privacy-preserving, and fair.

Call for papers of two related workshops:

  • ICDM 2022 Workshop on Advanced Neural Algorithms and Theories for Recommender System [NeuRec22]
  • KDD 2022 Workshop on Data Science and Artificial Intelligence for Responsible Recommendations [DS4RRS]
  • Topics of interest

    We invite contributions ranging from theoretical or conceptual papers to technical algorithmic ones as well as applications and case studies towards trustworthy recommendation, including but not limited to the following areas:

    • Fundamental or emerging data science or artificial intelligence theories, approaches and applications related to trustworthy recommendations
    • Recommendation with low-quality data, including highly sparse data, noisy or corrupted data, heavily duplicated data, and biased data
    • Uncertainty modeling for recommendation where user interests frequently drift over time and/or results need to be presented in a highly dynamic environment
    • Robustness models for recommendations including attacks and counter approaches
    • Interpretable recommendation that provides persuasive explanations and/or generates faithful interpretations to the recommendation process
    • Fairness and debiasing, where a fair system is designed to balance its accuracy with potential biases and/or unfairness
    • Security and privacy-aware recommendations including federated recommendation, on-device training/inference, and privacy-protected ranking mechanisms
    • Human-in-the-loop computing for improving accuracy, explainability, or adaptivity
    • Surveys, evaluations, or benchmarking on state-of-the-art research in the area of trustworthy recommendations
    • Novel and emerging applications of recommendation techniques, especially trustworthiness related approaches and solutions
    • Novel evaluation protocols, approaches and metrics for evaluating the trustworthiness of recommendations

    Important Dates

    Paper Submission: June 1, 2022    June 30, 2022(Extended)

    Paper Notification: July 31, 2022

    Paper Camera Ready Due: August 15, 2022

    All deadlines use the Anywhere on Earth (AoE) timezone

    Instructions for Authors

    Paper Length, Formatting, and Reviewing
  • The length of each paper submitted to the Research and Application tracks should be no more than 10 pages, whereas the maximum number of pages is 2 for each abstract submitted to the Poster and Journal track. Both types of papers should be formatted following the standard 2-column U.S. letter style of IEEE Conference template. See the IEEE Proceedings Author Guidelines:, for further information and instructions.
  • All submissions will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to the scope of the conference, originality, significance, and clarity. The names and affiliations of authors must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.
  • Authors are also encouraged to submit supplementary materials, i.e., providing the source code and data through a GitHub-like public repository to support the reproducibility of their research results.
  • Electronic submission site:
  • Important Policies
  • Reproducibility: The advancement of science depends heavily on reproducibility. We strongly recommend that the authors release their code and data to the public. Authors can provide an optional two-page supplement at the end of their submitted paper (it needs to be in the same PDF file and start at page 11). This supplement can only be used to include (i) information necessary for reproducing the experimental results reported in the paper (e.g., various algorithmic and model parameters and configurations, hyper parameter search spaces, details related to data set filtering and train/test splits, software versions, detailed hardware configuration, etc.), and (ii) any data, pseudo-code and proofs that could not be included in the main page of the manuscript due to space limitations. Papers with solid evidences for reproducibility are preferred for the Best Paper Award of a relevant track.
  • Authorship: The list of authors at the time of submission is considered final and any further changes of the authorship are not allowed.
  • Dual submissions: DSAA is an archival publication venue as such submissions that have been previously published, accepted, or are currently under consideration at other peer-review publication venues (i.e., journals, conferences, workshops with published proceedings, etc) are not permitted.
  • Conflicts of interest (COI): COIs must be declared at the time of submission. COIs include employment at the same institution within the past three years, collaborations during the past three years, advisor/advisee relationships, plus family and close friends. Program chairs, poster chairs, special session chairs, and tutorial chairs are not allowed to submit proposals to their organized tracks and sessions.
  • Attendance: At least one of the authors of each accepted paper must register in full and attend the conference to present the paper. No-show papers will be removed from the IEEE Xplore proceedings.
  • Submission Instructions:
    • Step 1: Login and enter DSAA conference in CMT3. Website: :
    • Step 2: Click the "Create new submission" button and then "Special Sessions".
    • Step 3: Enter your paper information and select "Data Science and Artificial Intelligence Enabled Trustworthy Recommendations" as the subject area.





    The tentative program committee members are listed below:


      Francesco Barile, Maastricht University, The Netherlands

      Saikishore Kalloori, ETC Zurich, Switzerland

      Marina Andric, Free University of Bozen-Bolzano, Italy

      Amra Delić, University of Sarajevo, Serbia

      Marko Tkalcic, University of Primorska in Koper, Slovenia

      Roberto Confalonieri, Free University of Bozen-Bolzano, Italy

      Floriano Zini, Free University of Bozen-Bolzano, Italy

      Massimo David, Free University of Bozen-Bolzano, Italy

      Defu Lian, University of Science and Technology of China, China

      Wenpeng Lu, Qilu University of Technology, China

      Yan Zhang, University of Technology Sydney, Australia

      YuChang Xu, Shanghai Jiaotong University, China

      Yong Liu, Nanyang Technological University, Singapore

      Trong Dinh Thac Do, University of Technology Sydney, Australia

      Shufeng Hao, Taiyuan University of Technology, China

      Quangui Zhang, Liaoning Technical University, China

      Wenzhuo Song, Jilin University, China

      Dhaval Patel, IBM Research, USA