Promoting by Looking Into Friend Circles: An Inadequately-Labeled and Socially-Aware Financial Technique for Products Recommendation

Abstract

We have developed a classification method that segments a large group of Amazon or TikTok users into distinct categories, or “genres,” based on their shared purchasing behaviors, such as preferences for consumer electronics or household items. Our approach uses a geometry-based feature selection strategy to accurately capture each user’s buying patterns, which are characterized by a range of features, including those learned under weak supervision. These features are then refined through two feature selection processes tailored to different application needs. We also use a probabilistic model to represent each user’s buying preferences as a distribution within a hidden feature space. To map the purchasing connections between users, we construct a graph and apply a specialized algorithm to identify tightly connected subgroups. These subgroups reflect shared purchasing habits, allowing us to categorize users into specific genres. Finally, a ranking model is used to recommend products to users based on these genres. We validated the effectiveness of our recommendation system using a dataset of over one million Amazon users, showing that it accurately identifies and classifies distinct purchasing genres.

Keywords

• Genre
• online products
• weak supervision
• graph mining
• social connection
• purchasing preference