Hybrid Recommender System Based on Personal Behavior Mining
원문 초록
Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional model with behavior pattern extraction method. We use desensitized mobile transaction record provided by T-mall, Alibaba to build a hybrid dynamic recommender system. The sequential pattern mining aims to find frequent sequential pattern in sequence database and is applied in this hybrid model to predict customers' payment behavior thus contributing to the accuracy of the model.
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Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
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