Enhancing Information Retrieval Effectiveness through Dynamic Query Expansion and Personalization

Enhancing Information Retrieval Effectiveness through Dynamic Query Expansion and Personalization

Registration ID: IJNRD_210255

Published ID: IJNRD2312018

DOI: Click Here to Get

Vaishnavi Santosh Ganeshkar , Himanshu Sangale , Sanskruti Pampattiwar , Mrs Mayura Kulkarni

information retrieval, personalization, relevance feedback, BERT, keybert, natural language processing, machine learning, search performance, user experience, search results

We address the problems associated with information retrieval on the internet in our research, "Enhancing Information Retrieval Effectiveness through Dynamic Query Expansion and Personalization," where large volumes of data can result in inconsistencies between search queries and pertinent content. Using machine learning techniques such as BERT and KeyBERT for optimal feature selection and similarity for accurate document-query matching, we propose an intelligent, personalized web search system. We used an SVM model to classify user interests, integrated a feedback module for customization, and implemented this in Python. Our findings show notable gains in the efficacy of information retrieval, providing more focused and customized search results across the extensive web information space.

Published Paper   E-Certificate

"Enhancing Information Retrieval Effectiveness through Dynamic Query Expansion and Personalization", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 12, page no.a106-a115, December-2023, Available :https://ijnrd.org/papers/IJNRD2312018.pdf

Volume 8 Issue 12, December-2023

Pages : a106-a115

Paper Reg. ID: IJNRD_210255

Published Paper Id: IJNRD2312018

Downloads: 000121143

Research Area: Engineering

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312018.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312018

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

Indexing In Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar | AI-Powered Research Tool, Microsoft Academic, Academia.edu, arXiv.org, Research Gate, CiteSeerX, ResearcherID Thomson Reuters, Mendeley : reference manager, DocStoc, ISSUU, Scribd, and many more

How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area