Context Aware Product Recommendation
2 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in Context Aware Product Recommendation
Most implemented papers
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
evison/JRL
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CIKM 2017
In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures.
Context-aware Retail Product Recommendation with Regularized Gradient Boosting
A total of 167 participants participated in the challenge, and we secured the 6th rank during the final evaluation with an MRR of 0. 4658 on the test set.
