Items by Chierichetti, Flavio

Web search engines use indexes to efficiently retrieve pages containing specified query terms, as well as pages linking to specified pages. The problem of compressed indexes that permit such fast retrieval has a long history. We consider the problem: assuming that the terms in (or links to) a page are generated from a probability distribution, how well compactly can we build such indexes that allow fast retrieval? Of particular interest is the case when the probability distribution is Zipfian (or a similar power law), since these are the distributions that arise on the web. We obtain sharp bounds on the space requirement of Boolean indexes for text documents that follow Zipf’s law. In the process we develop a general technique that applies to any probability distribution, not necessarily a power law; this is the first analysis of compression in indexes under arbitrary distributions. Our bounds lead to quantitative versions of rules of thumb that are folklore in indexing. Our experiments on several document collections show that the distribution of terms appears to follow a double-Pareto law rather than Zipf’s law. Despite widely varying sets of documents, the index sizes observed in the experiments conform well to our theoretical predictions.

Motivated by contextual advertising systems and other web applications involving efficiency–accuracy tradeoffs, we study similarity caching. Here, a cache hit is said to occur if the requested item is similar but not necessarily equal to some cached item. We study two objectives that dictate the efficiency–accuracy tradeoff and provide our caching policies for these objectives. By conducting extensive experiments on real data we show similarity caching can significantly improve the efficiency of contextual advertising systems, with minimal impact on accuracy. Inspired by the above, we propose a simple generative model that embodies two fundamental characteristics of page requests arriving to advertising systems, namely, long-range dependences and similarities. We provide theoretical bounds on the gains of similarity caching in this model and demonstrate these gains empirically by fitting the actual data to the model.