Optimizing a Recommender System for B2B Digital Marketplaces Based on Collaborative Filtering and Graph Networks
Abstract
Business-to-business (B2B) Digital Marketplaces are those that link businesses with suppliers, products, services, and business opportunities. Unlike consumer platforms, B2B platforms typically consist of few interactions, high dollar transactions, long sales cycles, complicated product attributes, organizational purchasing positions, negotiated pricing, and buyer-supplier-cataloged item relationships. These features can make the task of optimizing recommendation quality challenging with a single standard approach. In this paper, a hybrid recommender-system model fusing collaborative filtering with graph-based representation learning is proposed to enhance the relevance, discovery, and operational value in B2B marketplaces. Collaborative filtering looks at the patterns of buying–selling relations between buyers and items and graph networks are used to express multi-relational relations between organizations, items, suppliers, categories and transactions. The architecture proposed is data preparation, implicit-feedback modelling, graph construction, candidate generation, ranking, business-rule constraints and online monitoring. It covers metadata and graph connectivity for cold-start, and it includes temporal validation, ranking metrics, diversity, supplier exposure and commercial outcomes in evaluation. Since the paper does not include any market data, it does not contain fabricated experimental results, instead it proposes a research design and proposes some hypotheses. The anticipated delivery is a realistic framework of the benefits that graph-enhanced collaborative filtering can provide over basic approaches, in terms of measurable improvements in the metrics of explainability, privacy and marketplace fairness. The paper argues that recommender optimization must be a "whole-of-system decision problem," considering relevance, discovery, commerciality, system latency, and treating marketplace participants as appropriately.
