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Found 5 papers, 1 papers with code
Bayesian regularization of empirical MDPs
Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model.
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions.
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Previous queries in the user session can provide useful context for the user's intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user's prefix.
A Zero Attention Model for Personalized Product Search
In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine.
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
We further apply our algorithm to optimize a message that promotes adoption of an Amazon service.
