scholarly journals Integrating neurophysiologic relevance feedback in intent modeling for information retrieval

2019 ◽  
Vol 70 (9) ◽  
pp. 917-930 ◽  
Author(s):  
Giulio Jacucci ◽  
Oswald Barral ◽  
Pedram Daee ◽  
Markus Wenzel ◽  
Baris Serim ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Intent Modeling

Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval

2015 ◽  
Vol 5 (4) ◽  
pp. 31-45 ◽  
Author(s):  
Jagendra Singh ◽  
Aditi Sharan
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Contextual Information ◽  
Optimal Combination ◽  
Query Term ◽  
Benchmark Data ◽  
First Pass ◽  
Baseline Approach ◽  
Pseudo Relevance Feedback

Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment

2013 ◽  
pp. 193-204
Author(s):  
Ning Yu ◽  
Kien A. Hua ◽  
Danzhou Liu
Keyword(s):  
Information Retrieval ◽  
Mobile Devices ◽  
Relevance Feedback ◽  
Handheld Devices ◽  
System Response ◽  
Feedback Systems ◽  
Feedback Information ◽  
Battery Power ◽  
System Response Time ◽  
Client Side

During the last decade, high quality (i.e. over 1 megapixel) built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay, making the system unfriendly to use. This issue is addressed by considering a Client-side Relevance Feedback (CRF) technique. In the CRF system, Relevance Feedback (RF) is done on client side along. Mobile devices’ battery power is saved from exchanging images between server and client and system response is instantaneous, which significantly enhances system usability. Furthermore, because the server is not involved in RF processing, it is able to support more users simultaneously. The experiment indicates that the system outperforms the traditional server-client relevance feedback systems on the aspects of system response time, mobile battery power saving, and retrieval result.

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

2020 ◽  
pp. 20-39
Author(s):  
Fatiha Naouar ◽  
Lobna Hlaoua ◽  
Mohamed Nazih Omri
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Source ◽  
Strong Association ◽  
Relevant Information ◽  
User Query ◽  
Proposed Model ◽  
Evidence Source ◽  
Feedback Algorithm ◽  
Collaborative Information Retrieval

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.

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.

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