An effective ranking model should give a personalized ranking list for each user according to the user preference.
To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels.
We introduce deep learning models to the two most important stages in product search at JD. com, one of the largest e-commerce platforms in the world.
In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another.