Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The Hybrid Poisson Aspect Model for Personalized Shopping Recommendation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Editorial: Data Mining Lessons Learned
Machine Learning
Collaborative filtering on skewed datasets
Proceedings of the 17th international conference on World Wide Web
Using back-propagation to learn association rules for service personalization
Expert Systems with Applications: An International Journal
Grocery shopping recommendations based on basket-sensitive random walk
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
International Journal of Approximate Reasoning
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
HIS'12 Proceedings of the First international conference on Health Information Science
Learning to question: leveraging user preferences for shopping advice
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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A good shopping recommender system can boost sales in a retailer store. To provide accurate recommendation, the recommender needs to accurately predict a customer's preference, an ability difficult to acquire. Conventional data mining techniques, such as association rule mining and collaborative filtering, can generally be applied to this problem, but rarely produce satisfying results due to the skewness and sparsity of transaction data. In this paper, we report the lessons that we learned in two real-world data mining applications for personalized shopping recommendation. We learned that extending a collaborative filtering method based on ratings (e.g., GroupLens) to perform personalized shopping recommendation is not trivial and that it is not appropriate to apply association-rule based methods (e.g., the IBM SmartPad system) for large scale prediction of customers' shopping preferences. Instead, a probabilistic graphical model can be more effective in handling skewed and sparse data. By casting collaborative filtering algorithms in a probabilistic framework, we derived HyPAM (Hybrid Poisson Aspect Modelling), a novel probabilistic graphical model for personalized shopping recommendation. Experimental results show that HyPAM outperforms GroupLens and the IBM method by generating much more accurate predictions of what items a customer will actually purchase in the unseen test data. The data sets and the results are made available for download at http://chunnan.iis.sinica.edu.tw/hypam/HyPAM.html.