Adaptive Keywords Extraction with Contextual Bandits for Advertising on Parked Domains
This addresses the challenge for domain registrars and URL shortener providers to improve ad relevance and profits on parked domains, though it appears incremental as it builds on existing techniques.
The paper tackled the problem of generating relevant keywords for advertising on parked domains, where web content is missing, by introducing an algorithm based on BM25F term weighting and linear multi-armed bandits. The prototype was evaluated in a production system and shown to be more effective than other methods.
Domain name registrars and URL shortener service providers place advertisements on the parked domains (Internet domain names which are not in service) in order to generate profits. As the web contents have been removed, it is critical to make sure the displayed ads are directly related to the intents of the visitors who have been directed to the parked domains. Because of the missing contents in these domains, it is non-trivial to generate the keywords to describe the previous contents and therefore the users intents. In this paper we discuss the adaptive keywords extraction problem and introduce an algorithm based on the BM25F term weighting and linear multi-armed bandits. We built a prototype over a production domain registration system and evaluated it using crowdsourcing in multiple iterations. The prototype is compared with other popular methods and is shown to be more effective.