Authors - Kishore S, Jeganathan L, Janaki Meena M, Ummity Srinivasa Rao, Jayaram Balabaskaran Abstract - Finding movies from an enormous number of movies that fit our interests and preferences becomes a challenging endeavor. Because recommendation systems address information overload by recommending the most appropriate products to users, they have become widely used in today’s world. The majority of recommendation systems disregard the constraints of the user such as not suggesting certain exceptional movies to them because they aren’t as popular as others. Furthermore, the lack of transparency about how these recommendation algorithms operate creates concerns regarding accountability. In this work, we propose an improved ALS-based recommendation framework that is implemented on Apache Spark and uses HDFS for processing and storing data. In order to address the long tail bias problem, we utilize the ALSbased framework that enhances exposure to low-frequency items through strong interaction filtering. This study employs SHAP to improve transparency and facilitate fairness analysis by explaining the elements generating recommendations to overcome this limitation. Root Mean Square Error (RMSE) and Top-K long-tail exposure metrics are used to assess the model’s performance on a large movie interaction dataset.
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