Search (3 results, page 1 of 1)

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Abstract

Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query-independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek-Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state-of-the-art baseline models, the chronological term rank model and the Markov random field model.

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Abstract

Bibliometric-enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data-driven approach, this article describes the development of a bibliometric-enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric-enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known-item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.

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Abstract

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.