Recommender Systems for Implicit Feedback Datasets: Collaborative Filtering with Spark

Abstract

Recommender systems play a key role in delivering personalized content to users, with collaborative filtering being one of the most popular techniques. While traditional systems often rely on explicit feedback like user ratings, many real-world scenarios deal with implicit feedback—such as clicks, views, or purchase history—that’s more plentiful but less straightforward in showing user preferences. This paper dives into the challenges and methods of building effective recommendation systems using implicit feedback data. It focuses on collaborative filtering techniques and their application within Apache Spark, a robust framework for processing large-scale data. By utilizing Spark’s distributed computing power, we address scalability issues that arise when working with massive implicit datasets. We explore matrix factorization methods, like Alternating Least Squares (ALS), to model user-item interactions and improve the accuracy of recommendations. Additionally, the paper discusses optimization strategies to handle challenges like data sparsity and noise in implicit feedback. The effectiveness of these approaches is demonstrated through experiments with publicly available datasets, shedding light on how collaborative filtering can be applied in real-world systems.