Abstract Building on previous work in the field of language modeling information retrieval (IR), this paper proposes a novel approach to document ranking based on statistical model selection. The proposed approach offers two main contributions. First, we posit the notion of a document’s “null model,” a language model that conditions our assessment of the document model’s significance with respe...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas, Fiat, Karlin and McSherry [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and p...

In this paper we report our approach and result as a participant of the NTCIR-9 Intent task. INTENT task is a new NTCIR task which consists of two subtasks: (1) Subtopic Mining subtask: given a query, a system lists all possible subtopics that might cover users’ different intents. Our approach is mining the query log to find subtopics candidates and rank them according to the frequencies of eac...

1* Min Zhao is currently researcher at NEC Lab China, Beijing. Abstract. This paper proposes a new approach to ranking the documents retrieved by a search engine using click-through data. The goal is to make the final ranked list of documents accurately represent users’ preferences reflected in the click-through data. Our approach combines the ranking result of a traditional IR algorithm (BM25)...

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with th...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas et al. [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and provide some prelimin...

We study personalized web ranking algorithms based on the existence of document clusterings. Motivated by the topic sensitive page ranking of Haveliwala [20], we develop and implement an efficient “local-cluster” algorithm by extending the web search algorithm of Achlioptas et al. [10]. We propose some formal criteria for evaluating such personalized ranking algorithms and provide some prelimin...

In this paper, we propose the new Ball Ranking Machines (BRMs) to address the supervised ranking problems. In previous work, supervised ranking methods have been successfully applied in various information retrieval tasks. Among these methodologies, the Ranking Support Vector Machines (Rank SVMs) are well investigated. However, one major fact limiting their applications is that Ranking SVMs nee...

Ranking is a central problem in information retrieval. Modern search engines, especially those designed for the World Wide Web, commonly analyze and combine hundreds of features extracted from the submitted query and underlying documents in order to assess the relative relevance of a document to a given query and thus rank the underlying collection. The sheer size of this problem has led to the...

In this paper, we describe our participation in the INEX 2012 Social Book Search track. We investigate the contribution of different types of document metadata, both social and controlled, and examine the effectiveness of re-ranking retrieval results using different social features, such as user ratings, tags, and authorship information. We find that the best results are obtained using all avai...