Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Consumer Sequential Search: Not Enough or Too Much?
Marketing Science
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Dynamic Conversion Behavior at E-Commerce Sites
Management Science
IEEE Transactions on Knowledge and Data Engineering
An MDP-Based Recommender System
The Journal of Machine Learning Research
Personalization technologies: a process-oriented perspective
Communications of the ACM - The digital society
A new approach to classification based on association rule mining
Decision Support Systems
Examination of online channel preference: using the structure-conduct-outcome framework
Decision Support Systems
Design of a shopbot and recommender system for bundle purchases
Decision Support Systems
INFORMS Journal on Computing
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Modeling Online Browsing and Path Analysis Using Clickstream Data
Marketing Science
Shopbot 2.0: Integrating recommendations and promotions with comparison shopping
Decision Support Systems
Selling or Advertising: Strategies for Providing Digital Media Online
Journal of Management Information Systems
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Collaborative filtering with temporal dynamics
Communications of the ACM
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Using external aggregate ratings for improving individual recommendations
ACM Transactions on the Web (TWEB)
Consideration set generation in commerce search
Proceedings of the 20th international conference on World wide web
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
A Dynamic Model of Sponsored Search Advertising
Marketing Science
Active Machine Learning for Consideration Heuristics
Marketing Science
Collaborative user modeling for enhanced content filtering in recommender systems
Decision Support Systems
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With rapid advances in e-commerce applications and technologies, finding the chance that a product falls into a consumer's consideration set after being inspected (i.e., consideration probability, CP) becomes an important issue of recommendation services and marketing strategies for both academia and practitioners. This paper proposes a novel business intelligence (BI) approach (namely, the two-step estimation approach, TEA) to estimating CPs with a two-step procedure: one is to introduce partial belongings of consumers to the latent classes with both positive and negative preferences (tastes); the other step is to generate CPs based on the degrees of partial belongings in a weighted probability manner. Experiment results from different online shopping scenarios reveal that TEA is effective and outperforms the traditional latent class model.