The goal of search personalization is to tailor search results to individual users by taking into account their profiles, which include their particular interests and preferences. As these latter are multiple and changing over time, personalization becomes effective when the search process takes into account the current user interest. This paper presents a search personalization approach that m...

This paper describes our participation in the Intent Mining track of NTCIR-11. We present our methods and results for both document ranking and subtopic mining. Our ranking methods are based on several data fusion techniques with some variations. Our subtopic mining method is a very simple technique that uses query dimensions’ items to form a subtopic

Information retrieval from web and XML document collections is ever more focused on returning entities instead of web pages or XML elements. There are many research fields involving named entities; one such field is known as entity ranking, where one goal is to rank entities in response to a query supported with a short list of entity examples. In this paper, we describe our approach to ranking...

The main goal of search engines is ad hoc retrieval: ranking documents in a corpus by their relevance to the information need expressed by a query. The Probability Ranking Principle (PRP) — ranking the documents by their relevance probabilities — is the theoretical foundation of most existing ad hoc document retrieval methods. A key observation that motivates our work is that the PRP does not a...

This paper describes our participation in the GeoCLEF monolingual English task of the Cross Language Evaluation Forum 2006. The main objective of this study is to evaluate the retrieve performance of our geographic information retrieval system. The system consists of four modules: the geographic knowledge base that provides information about important geographic entities around the world and re...

We investigate the application of a novel relevance ranking technique, cover density ranking, to the requirements of Web-based information retrieval, where a typical query consists of a few search terms and a typical result consists of a page indicating several potentially relevant documents. Traditional ranking methods for information retrieval, based on term and inverse document frequencies, ...

While the Probability Ranking Principle for Information Retrieval provides the basis for formal models, it makes a very strong assumption regarding the dependence between documents. However, it has been observed that in real situations this assumption does not always hold. In this paper we propose a reformulation of the Probability Ranking Principle based on quantum theory. Quantum probability ...

Current learning to rank approaches commonly focus on learning the best possible ranking function given a small fixed set of documents. This document set is often retrieved from the collection using a simple unsupervised bag-of-words method, e.g. BM25. This can potentially lead to learning a sub-optimal ranking, since many relevant documents may be excluded from the initially retrieved set. In ...

An essential issue in document retrieval is ranking, which is used to rank documents by their relevancies to a given query. This paper presents a novel machine learning framework for ranking based on document groups. Multiple level labels represent the relevance of documents. The values of labels are used to quantify the relevance of the documents. According to a given query in the training set...

This paper presents the technique details and experimental results of the information retrieval system with which we participated at the NTCIR-8 ACLIA (Advanced Cross-language Information Access) IR4QA (Information Retrieval for Question Answering) task. Document corpus in Simplified Chinese (CS) and Traditional Chinese (CT) with topics in English, CS and CT were used in our experiments. We com...