A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations

Recommender systems are used in e-Commerce websites to make product recommendations or deliver personalized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems - user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.

Automated summary

This research investigates the impact of consumer knowledge on the performance and evaluation of user-based collaborative filtering and content-based recommendation systems. Results indicate that expert consumers prefer user-based collaborative filtering systems, while there is no significant preference difference between the two systems among novice consumers.