Recommendation Engine for Amazon Magazine Subscriptions

Recommender systems play a crucial role in enhancing user experience and engagement on e-commerce plat-forms by suggesting relevant products based on user behavior. In the context of Amazon’s extensive catalog of over 8,000 magazines spanning more than twenty-five categories, providing personalized magazine subscription recommendations poses a significant challenge. This study addresses the problem of identifying potential future associations between magazine reviewers and products using a graph-based approach. Specifically, we aim to predict unseen but likely links between users and magazines to improve recommendation quality. To achieve this, we construct an undirected bipartite network connecting reviewers and magazine products based on review data. We perform network analysis using measures such as centrality, modularity, and clustering, and apply sentiment analysis and topic modeling to extract behavioral and thematic insights from user reviews. These insights inform a series of link prediction techniques including Common Neighbors, Adamic-Adar, Jaccard Coefficient, and Preferential Attachment evaluated using cross-validation and ROC curves. Our results show that the Preferential Attachment model outperforms other approaches, attributed to the skewed degree distribution inherent in the dataset’s structure.