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@article{160819,
        author = {Drashti Shrimal and Dr. Harshali Patil},
        title = {Opacity to Transparency: A Journey into Explainable Recommender Systems },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {1617-1626},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160819},
        abstract = {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.},
        keywords = {Explainable Recommender systems, User trust, User engagement.},
        month = {},
        }