Poster
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
Andrey Kolobov · Yuval Peres · Cheng Lu · Eric Horvitz
East Exhibition Hall B, C #213
Keywords: [ Convex Optimization ] [ Optimization ] [ Web Applications and Internet Data; Reinforcement Learning and Planning ] [ Applications -> Information Retrieval; Applications ]
From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e.g., web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18.5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art.
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