Adaptive Information Retrieval Based on Task Context

2011 ◽  
pp. 161-184
Author(s):  
Bich-Liên Doan ◽  
Jean-Paul Sansonnet
Keyword(s):  
Information Retrieval ◽  
State Of The Art ◽  
Contextual Information ◽  
Task Context ◽  
Research Directions ◽  
Query Process ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Adaptive Information Retrieval ◽  
Intelligent Assistant

This chapter discusses using context in Information Retrieval systems and Intelligent Assistant Agents in order to improve the performance of these systems. The notion of context is introduced and the state of the art in Contextual Information Retrieval is presented which illustrates various categories of contexts that can be taken into account when solving user queries. In this framework, the authors focus on the issue of task-based context which takes into account the current activity the user is involved in when he puts a query. Finally they introduce promising research directions that promote the use of Intelligent Assistant Agents capable of symbolic reasoning about users’ tasks for supporting the query process.

Spatio-Temporal Based Personalization for Mobile Search

2012 ◽  
pp. 386-409 ◽  
Author(s):  
Ourdia Bouidghaghen ◽  
Lynda Tamine
Keyword(s):  
Information Retrieval ◽  
Contextual Information ◽  
The Internet ◽  
Search Results ◽  
Search Result ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Spatio Temporal ◽  
Mobile Context

The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user’s search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.

User Models for Adaptive Information Retrieval on the Web

2012 ◽  
Vol 3 (3) ◽  
pp. 1-19
Author(s):  
Max Chevalier ◽  
Christine Julien ◽  
Chantal Soulé-Dupuy
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
User Profile ◽  
User Model ◽  
User Models ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Adaptive Information Retrieval ◽  
The Web

Searching information can be realized thanks to specific tools called Information Retrieval Systems IRS (also called “search engines”). To provide more accurate results to users, most of such systems offer personalization features. To do this, each system models a user in order to adapt search results that will be displayed. In a multi-application context (e.g., when using several search engines for a unique query), personalization techniques can be considered as limited because the user model (also called profile) is incomplete since it does not exploit actions/queries coming from other search engines. So, sharing user models between several search engines is a challenge in order to provide more efficient personalization techniques. A semantic architecture for user profile interoperability is proposed to reach this goal. This architecture is also important because it can be used in many other contexts to share various resources models, for instance a document model, between applications. It is also ensuring the possibility for every system to keep its own representation of each resource while providing a solution to easily share it.

Method of Lexical Enrichment in Information Retrieval System in Arabic

2013 ◽  
Vol 3 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Souheyl Mallat ◽  
Anis Zouaghi ◽  
Emna Hkiri ◽  
Mounir Zrigui
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Semantic Analysis ◽  
Contextual Information ◽  
Information Retrieval System ◽  
Enrichment Method ◽  
Weighting Functions ◽  
Retrieval Systems ◽  
Significant Term ◽  
Information Retrieval Systems

In this paper, the authors propose a method for lexical enrichment of Arabic queries in order to improve the performance of the information retrieval systems SRI. This method has two types of enrichment: linguistic and contextual. The first one is based on the linguistic analysis (lemmatization, morphological, syntactic and semantic analysis), whose goal is to generate a descriptive list (list-desc). This list contains a set of linguistic lexicon assigned to each significant term in the query. The second enrichment consists in integrating contextual information derived from the corpus documents. It is based on statistical analysis using Salton weighting functions: TF-IDF and TF-IEF. The TF-IDF function is applied on the list-desc and documents in the corpus in order to identify relevant documents. TF-IEF function is made between the list-desc and sentences belonging to the relevant documents to identify relevant sentences. Then, terms in these sentences are weighted, and those with highest weights are considered rich in terms of informative and contextual importance are added to the original query. The authors' lexical enrichment method was evaluated on a corpus of documents belonging to a specialized domain and results show its interest in terms of precision and recall.

scholarly journals State-of-the-Art Review on Relevance of Genetic Algorithm to Internet Web Search

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Kehinde Agbele ◽  
Ademola Adesina ◽  
Daniel Ekong ◽  
Oluwafemi Ayangbekun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Information Needs ◽  
Web Search ◽  
State Of The Art ◽  
Retrieval Systems ◽  
Online Databases ◽  
Information Retrieval Systems ◽  
Survival Of The Fittest

People use search engines to find information they desire with the aim that their information needs will be met. Information retrieval (IR) is a field that is concerned primarily with the searching and retrieving of information in the documents and also searching the search engine, online databases, and Internet. Genetic algorithms (GAs) are robust, efficient, and optimizated methods in a wide area of search problems motivated by Darwin’s principles of natural selection and survival of the fittest. This paper describes information retrieval systems (IRS) components. This paper looks at how GAs can be applied in the field of IR and specifically the relevance of genetic algorithms to internet web search. Finally, from the proposals surveyed it turns out that GA is applied to diverse problem fields of internet web search.

Relevance Predictability in Information Retrieval Systems

1967 ◽  
Vol 06 (02) ◽  
pp. 45-51 ◽  
Author(s):  
A. Kent ◽  
J. Belzer ◽  
M. Kuhfeerst ◽  
E. D. Dym ◽  
D. L. Shirey ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Experimental Conditions ◽  
Treatment Groups ◽  
Retrieval Systems ◽  
Significant Difference ◽  
Information Retrieval Systems ◽  
High Predictability ◽  
Intermediate Response ◽  
Quantitative Indicators ◽  
Level Of Processing

An experiment is described which attempts to derive quantitative indicators regarding the potential relevance predictability of the intermediate stimuli used to represent documents in information retrieval systems. In effect, since the decision to peruse an entire document is often predicated upon the examination of one »level of processing« of the document (e.g., the citation and/or abstract), it became interesting to analyze the properties of what constitutes »relevance«. However, prior to such an analysis, an even more elementary step had to be made, namely, to determine what portions of a document should be examined.An evaluation of the ability of intermediate response products (IRPs), functioning as cues to the information content of full documents, to predict the relevance determination that would be subsequently made on these documents by motivated users of information retrieval systems, was made under controlled experimental conditions. The hypothesis that there might be other intermediate response products (selected extracts from the document, i.e., first paragraph, last paragraph, and the combination of first and last paragraph), that would be as representative of the full document as the traditional IRPs (citation and abstract) was tested systematically. The results showed that:1. there is no significant difference among the several IRP treatment groups on the number of cue evaluations of relevancy which match the subsequent user relevancy decision on the document;2. first and last paragraph combinations have consistently predicted relevancy to a higher degree than the other IRPs;3. abstracts were undistinguished as predictors; and4. the apparent high predictability rating for citations was not substantive.Some of these results are quite different than would be expected from previous work with unmotivated subjects.

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