Revisiting Bundle Recommendation for Intent-aware Product Bundling

Author:

Sun Zhu1ORCID,Feng Kaidong2ORCID,Yang Jie3ORCID,Fang Hui4ORCID,Qu Xinghua5ORCID,Ong Yew-Soon1ORCID,Liu Wenyuan2ORCID

Affiliation:

1. A*STAR Centre for Frontier AI Research, Nanyang Technological University, Singapore, Singapore

2. Yanshan University, Qinhuangdao, China

3. Delft University of Technology, Delft, Netherlands

4. Shanghai University of Finance and Economics, Shanghai China

5. Shanda AI Lab, Singapore Singapore

Abstract

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.

Funder

National Natural Science Foundation of China

Shanghai Rising-Star Program

Natural Science Foundation of Shanghai

Delft Design@Scale AI Lab

Publisher

Association for Computing Machinery (ACM)

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