With the rapid growth of Internet e-commerce, recommendation systems have become key components for online platforms to provide efficient, accurate, and personalized user and product suggestions. These recommendations directly improve user experience, increase user retention, and drive sales growth. Incorporating social relationships into the traditional user-product interaction network has been widely proven to enhance recommendation quality and enable innovative scenarios such as friend and sharing-based recommendations. However, mainstream social e-commerce recommendation methods face significant limitations: they rely heavily on external social data from third-party platforms, which are often difficult to fully access due to privacy policies, platform restrictions, and data silo issues. Moreover, most existing solutions have only been tested on datasets with millions of users and struggle to scale to hundreds of millions due to high computational costs and limited user coverage—posing substantial barriers to their deployment on large-scale e-commerce platforms. To overcome these challenges, this study focuses on automatically building a user relationship network with clear real-world social meanings, using only internal behavioral data from e-commerce platforms without external social information. The main goals are to develop a scalable self-supervised method for inferring social relationships among large user bases, improve the efficiency of user relationship prediction at the scale of hundreds of millions, enrich the understanding of user preferences at the platform level, and expand the performance and application scope of e-commerce recommendation systems.

Based on the homophily principle from communication studies, the proposed framework includes four sequential and interrelated stages: pseudo-label network construction, user relationship inference, efficient candidate matching, and relationship type inference. First, two typical behavioral signals—co-purchase behavior and spatiotemporal co-occurrence—are extracted from e-commerce logs to build pseudo-label social networks that reflect family ties and geographic connections, respectively, serving as weak supervision signals. Next, a user relationship inference model based on multilayer perceptrons is designed to learn user representations from these networks; positive samples are obtained from observed pseudo-label edges, while negative samples are generated by random pairing of users, and the model is trained using binary cross-entropy loss. To address the high computational demand of examining all user pairs in billion-scale scenarios, an efficient candidate matching strategy based on multilevel clustering of user embeddings is proposed, significantly reducing the number of candidate pairs while maintaining high recall. Lastly, a multitask inference module is built to first predict whether a candidate pair has an actual social connection, then classify the relationship into five detailed types—senior-junior, spouse, neighbor, schoolmate, and colleague—using rules that combine pseudo-labels with user attributes such as age, gender, time, and location.

Extensive experiments on real data from a large e-commerce platform (Company T) show that co-purchase relationship prediction achieves a precision of 71.70%, a recall of 87.44%, an accuracy of 76.49%, and an F1-score of 0.79. The multilevel clustering candidate matching strategy reduces computational load and supports stable online deployment at the scale of hundreds of millions of users. Relationship classification reaches high precision: 93.80% for spouses and 64.57% for senior-junior relations. The resulting heterogeneous social graph includes billions of edges across five relationship types, and online A/B tests confirm that incorporating social relationship information into recommendation models significantly improves accuracy, especially for category-sensitive items like medical products.

This research offers a practical solution for social e-commerce recommendations without relying on external social data, addressing privacy and platform restrictions. It enables the automatic construction of semantically rich social graphs using only internal behavioral data and supports large-scale applications through efficient clustering-based candidate matching. The proposed framework effectively incorporates social semantics into traditional recommendation systems, enhances user preference modeling, and boosts recommendation accuracy. This study not only demonstrates that the homophily principle applies to e-commerce behavior analysis but also provides scalable, interpretable methods for building large-scale social graphs and improving socially aware recommendations in real-world industry scenarios.