Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems.
product-recommendation
Context Aware Product Recommendation
3260 papers • 126 benchmarks • 313 datasets
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Most implemented papers
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
W. Bruce Croft, Yongfeng Zhang, Qingyao Ai, Xu Chen•Sun Nov 05 2017
This work analyzes how information propagates among different information sources in a gradient-descent learning paradigm, and proposes an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice.
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