Modeling Buying Motives for Personalized Product Bundle Recommendation
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.
G. Liu et al., "Modeling Buying Motives for Personalized Product Bundle Recommendation," ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3, Association for Computing Machinery (ACM), Apr 2017.
The definitive version is available at https://doi.org/10.1145/3022185
Keywords and Phrases
Algorithms; Behavioral research; Data mining; Design; Digital storage; Graph theory; Information filtering; Information management; Marketing; Buying motives; Experimentation; H.2.8 [database management]: database applications - data minings; Information storage and retrieval; Probabilistic graphical models; Product bundle; Recommendation; Product design; Design; H.3.3 [information storage and retrieval]: information filtering - recommendation
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Association for Computing Machinery (ACM), All rights reserved.