Modeling Buying Motives for Personalized Product Bundle Recommendation

TLDR

Product bundling groups multiple items into a single offer, yet how to align bundles with consumer preferences and buying motives remains underexplored. The study develops a probabilistic model that captures the relationships among items in a bundle by introducing latent node‑type and edge‑type factors. Bundles are represented as graphs with items as nodes and pairwise associations as edges, and the model’s inferred preferences are used to recommend items likely to be purchased together. Experiments on real‑world transaction data demonstrate that the proposed method outperforms baseline approaches and explains consumers’ buying motives through node‑ and edge‑type distinctions.

Abstract

Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers’ buying motives for product bundles in terms of different node-types and edge-types .