Classic IR (information retrieval) is inherently predicated on users searching for information, the socalled "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me

Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text

Web search engines generally treat search requests in isolation. The results for a given query are identical, independent of the user, or the context in which the user made the request. Nextgeneration search engines will make increasing use of context information, either by using explicit

This article presents a high-level discussion of some problems in information retrieval that are unique to web search engines. The goal is to raise awareness and stimulate research in these areas.

The Web today plays a central part in the cultural, educational and commercial life of millions of users. Due to the astonishing amount of information available on the Web, users typically rely on the Web search engines in order to locate relevant and useful information. A Web search engine’s task

We evaluate three different relevance feedback (RF) algorithms, Rocchio, Robertson/Sparck-Jones (RSJ) and Bayesian, in the context of Web search. We use a target-testing experimental procedure whereby a user must locate a specific document. For user relevance feedback, we consider all possible user

In web search, surfers are often faced with the problem of selecting their most wanted information from the potential huge amount of search results. The clustering of web search results is the possible solution, but the traditional content based clustering is not sufficient since it ignores many

The paper describes a new method of constructing semantic expansions of search requests for improving the results of Web search. This method is based on the theory of K-representations- a new theory of designing semantic-syntactic analyzers of natural language texts with the broad use of formal

setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle