Cognitive approach for building user model in an information retrieval context (poster session)

2000 ◽  
Author(s):  
Amina Sayeb Belhassen ◽  
Nabil Ben Abdallah ◽  
Henda Hadjami Ben Ghezala
Keyword(s):  
Information Retrieval ◽  
Poster Session ◽  
User Model ◽  
Cognitive Approach

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.

Modeling Users for Adaptive Information Retrieval by Capturing User Intent

2011 ◽  
pp. 88-118 ◽  
Author(s):  
Eugene Santos Jr. ◽  
Hien Nguyen
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Seeking ◽  
Query Language ◽  
Traditional Approach ◽  
User Model ◽  
Evaluation Methodology ◽  
User Intent ◽  
Document Frequency ◽  
Frequency Weighting

In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a user’s intent is captured and used for improving his/her effectiveness in an information seeking task. The user intent is captured by analyzing the commonality of the retrieved relevant documents. The effectiveness of our user model is evaluated with regards to retrieval performance using an evaluation methodology which allows us to compare with the existing approaches from the information retrieval community while assessing the new features offered by our user model. We compare our approach with the Ide dec-hi approach using term frequency inverted document frequency weighting which is considered to be the best traditional approach to relevance feedback. We use CRANFIELD, CACM and MEDLINE collections which are very popular collections from the information retrieval community to evaluate relevance feedback techniques. The results show that our approach performs better in the initial runs and works competitively with Ide dec-hi in the feedback runs. Additionally, we evaluate the effects of our user modeling approach with human analysts. The results show that our approach retrieves more relevant documents to a specific analyst compared to keyword-based information retrieval application called Verity Query Language.

Intelligent Information Personalization

2011 ◽  
pp. 118-146 ◽  
Author(s):  
Syed Sibte Raza Abidi
Keyword(s):  
Information Retrieval ◽  
User Model ◽  
Functional Requirements ◽  
Future Outlook ◽  
Web Based ◽  
Level Information ◽  
Intelligent Information ◽  
Intelligent Methods ◽  
Traditional Approaches ◽  
Information Personalization

This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based on an individual’s interests, needs and goals. We present intelligent techniques to dynamically compose new personalized information by adapting existing web-based information in line with a dynamic user-model, whilst simultaneously addressing linguistic, factual and functional requirements. This chapter will highlight the different facets, tasks and issues concerning intelligent information personalization to guide researchers in designing intelligent information personalization applications. The chapter presents intelligent methods that address information personalization at the content level as opposed to the traditional approaches that focus on interface level information personalization. To assist researchers in designing intelligent information personalization applications we present our information personalization framework, named AdWISE (Adaptive Webmediated Information and Services Environment), to demonstrate how to systematically integrate various intelligent methods to achieve information personalization. We will conclude with a commentary on the future outlook for intelligent information personalization.

scholarly journals Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

2019 ◽  
Vol 53 (2) ◽  
pp. 11-19
Author(s):  
Jon Degenhardt ◽  
Surya Kallumadi ◽  
Utkarsh Porwal ◽  
Andrew Trotman
Keyword(s):  
Information Retrieval ◽  
Real World ◽  
Poster Session ◽  
Panel Discussion ◽  
Product Search ◽  
Full Day

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a platform for publication and discussion of Information Retrieval and NLP research and their applications in the domain of eCommerce. The workshop program was designed to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in the eCommerce domain. A second goal was to run a data challence on real-world eCommerce data. The workshop drew contributions from both industry as well as academia, in total the workshop received 38 submissions, and accepted 24 (63%). There were two keynotes by invited speakers, a poster session where all the accepted submissions were presented, a panel discussion, and three short talks by invited speakers.

scholarly journals Topic Evolution of Library and Information Science Studies in the Last Twenty Years: A Co-Word Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 55-68
Author(s):  
Shafi Habibi ◽  
Parvin Abdollahzadeh ◽  
Mohammadhiwa Abdekhoda ◽  
Hossein Aghayari
Keyword(s):  
Information Retrieval ◽  
Information Science ◽  
Science Studies ◽  
Academic Disciplines ◽  
Library And Information Science ◽  
Cognitive Approach ◽  
Science Field ◽  
Scientific Disciplines ◽  
The Relationship ◽  
Emerging Topics

Abstract Background and Objectives: Topic evolutions of scientific and academic disciplines can be clarified by drawing scientific maps and identifies emerging or developed topics of scientific disciplines, sub-topics and the relationship between different topics of a discipline. The purpose of this study is to draw a strategic diagram to analyze the developments of the last twenty years of library and information science field. Material and Methods: This was a Scientometrics study with co-occurrence analysis of words that was conducted on ten journals with the highest impact factor in the field of library and information science. Twenty years (1998-2017) publications were extracted from LISTA database and imported into SciMAT software. After preparing the data, all publications were divided into four time periods, strategic diagrams of each period were drawn and topic clusters were analyzed. Results: The largest clusters of the first two periods were "Information Retrieval" and "Bibliometrics", which in the next period "Citation-Analysis" appeared instead of "Information Retrieval" cluster, but nevertheless the largest node of this cluster was "Information Retrieval". These clusters were the most developed topics in the field of library and information science. Conclusion: Information retrieval and bibliometrics are at the forefront of library and information science. Sentiment analysis and information literacy with a cognitive approach are emerging topics in the field. Also, studies related to information production and related indicators have led to qualitative research.

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