Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning
This work addresses the challenge of improving keyword recommendation strategies for advertisers in sponsored search, offering a novel approach to handle competition among keywords, though it is incremental in its application of existing methods to this specific domain.
The paper tackles the problem of selecting optimal keyword combinations for sponsored search advertising by formulating it as a combinatorial optimization problem, using a modified pointer network trained with deep reinforcement learning and achieving remarkable improvements in offline and online evaluations.
In sponsored search, keyword recommendations help advertisers to achieve much better performance within limited budget. Many works have been done to mine numerous candidate keywords from search logs or landing pages. However, the strategy to select from given candidates remains to be improved. The existing relevance-based, popularity-based and regular combinatorial strategies fail to take the internal or external competitions among keywords into consideration. In this paper, we regard keyword recommendations as a combinatorial optimization problem and solve it with a modified pointer network structure. The model is trained on an actor-critic based deep reinforcement learning framework. A pre-clustering method called Equal Size K-Means is proposed to accelerate the training and testing procedure on the framework by reducing the action space. The performance of framework is evaluated both in offline and online environments, and remarkable improvements can be observed.