Opacity to Transparency: A Journey into Explainable Recommender Systems
Drashti Shrimal, Dr. Harshali Patil
Explainable Recommender systems, User trust, User engagement.
Recommender systems have become an integral part of our daily lives, assisting users in discovering the relevant products, services, and information. However, the lack of transparency in the decision-making process of traditional recommender systems has raised concerns regarding user trust and understanding. To address this issue, explainable recommender systems have emerged as a promising research direction. This paper provides a comprehensive overview of explainable recommender systems, focusing on their significance & need, areas of usability, rationale, expected outcomes and challenges. Further it provides a comparative analysis of three approaches used in XAI. To provide a holistic perspective, we review existing literature and highlight key research trends and open challenges in the field of explainable recommender systems. Overall, this paper aims to serve as a comprehensive resource for researchers, practitioners, and decision-makers interested in understanding the state of the art in explainable recommender systems.
Article Details
Unique Paper ID: 160819

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 1617 - 1626
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