We present SETS, an architecture for efficient search in peer-to-peer networks, building upon ideas drawn from machine learning and social network theory. The key idea is to arran...
This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from larges...
In this paper, we consider the problem of identifying and segmenting topically cohesive regions in the URL tree of a large website. Each page of the website is assumed to have a t...
In this paper, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. Compared with previous ones, our work has four disti...
While classic information retrieval methods return whole documents as a result of a query, many information demands would be better satisfied by fine-grain access inside the docu...