Effects of prior knowledge on the effectiveness of a hybrid user model for information retrieval

2007 ◽  
Author(s):  
Hien Nguyen ◽  
Eugene Santos, Jr.
Keyword(s):  
Information Retrieval ◽  
Prior Knowledge ◽  
User Model

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 A New User Model based Interactive Product Retrieval Process for improved eBuying

2015 ◽  
Vol 19 ◽  
Author(s):  
Oshadi Alahakoon
Keyword(s):  
Information Retrieval ◽  
Recommender Systems ◽  
User Model ◽  
User Preferences ◽  
Retrieval Algorithm ◽  
Retrieval Process ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems ◽  
Interactive Product

When searching for items online there are three common problems that e-buyers may encounter; null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. In the past information retrieval systems or recommender systems were used as solutions. With information retrieval systems, too rigorous filtering based on the user query to reduce unmanageable number of items result in either null retrieval or filtering out the items users prefer. Recommender systems on the other hand do not provide sufficient opportunity for users to communicate their needs. As a solution, this paper introduces a novel method combining a user model with an interactive product retrieval process. The new layered user model has the potential of being applied across multiple product and service domains and is able to adapt to changing user preferences. The new product retrieval algorithm is integrated with the user model and is able to successfully address null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. The process is demonstrated using a bench mark dataset and a case study. Finally the Product retrieval process is evaluated using a set of guidelines to illustrate its suitability to current eBuying environments.

Demonstration of the Use of Tape-Recorded Information from a High Vacuum Electron Microscope

1969 ◽  
Vol 27 ◽  
pp. 218-219
Author(s):  
Richard E. Hartman ◽  
Roberta S. Hartman ◽  
Peter L. Ramos
Keyword(s):  
Information Retrieval ◽  
Electron Microscope ◽  
Magnetic Tape ◽  
High Vacuum ◽  
Data Retrieval ◽  
Electronic Information ◽  
Electronic Data ◽  
Subsequent Processing ◽  
Continuous Record

We have long felt that some form of electronic information retrieval would be more desirable than conventional photographic methods in a high vacuum electron microscope for various reasons. The most obvious of these is the fact that with electronic data retrieval the major source of gas load is removed from the instrument. An equally important reason is that if any subsequent analysis of the data is to be made, a continuous record on magnetic tape gives a much larger quantity of data and gives it in a form far more satisfactory for subsequent processing.

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