Social networks are an essential component of people'daily lives, and as a result, much
academic attention has been focused on them. The rapid adoption of machine learning as a …

The cluster hypothesis is a fundamental concept in ad hoc retrieval. Heretofore, cluster
hypothesis tests were applied to documents using binary relevance judgments. We present …

Due to the boundless expansion of the Web in the last decade, the research community has
paid significant attention to the problem of effective searching in the vast information …

Reading a document can often trigger a need for additional information. For example, a
reader of a news article might be interested in information about the persons and events …

In this paper, we describe a novel framework for improving information retrieval results. At
first, relevant documents are organized in clusters utilizing the containment metric along with …

The merits of using multiple queries representing the same information need to improve
retrieval effectiveness have recently been demonstrated in several studies. In this paper we …

As a major type of unsupervised machine learning method, clustering has been widely
applied in various tasks. Different clustering methods have different characteristics …

In relevance feedback, first-round search results are used to boost second-round search
results. Two forms have been traditionally considered: exhaustively labelled feedback …

We present a novel approach for efficiently evaluating the performance of retrieval models
and introduce two evaluation metrics: Distributional Overlap (DO), which compares the …

Document clustering in information retrieval (IR) is considered an alternative to rank-based
retrieval approaches, because of its potential to support user interactions beyond just typing …