Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Case Study in Information Delivery to Mass Retail Markets
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Enhancing Product Recommender Systems on Sparse Binary Data
Data Mining and Knowledge Discovery
Predicting customer shopping lists from point-of-sale purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Using ubiquitous computing in interactive mobile marketing
Personal and Ubiquitous Computing
Product retrieval for grocery stores
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Natural language retrieval of grocery products
Proceedings of the 17th ACM conference on Information and knowledge management
Why promotion strategies based on market basket analysis do not work
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Shopping lists play a central role in grocery shopping. Among other things, shopping lists serve as memory aids and as a tool for budgeting. More interestingly, shopping lists serve as an expression and indication of customer needs and interests. Accordingly, shopping lists can be used as an input for recommendation techniques. In this paper we describe a methodology for making recommendations about additional products to purchase using items on the user's shopping list. As shopping list entries seldom correspond to products, we first use information retrieval techniques to map the shopping list entries into candidate products. Association rules are used to generate recommendations based on the candidate products. We evaluate the usefulness and interestingness of the recommendations in a user study.