scholarly journals Modeling User Search Behavior for Masquerade Detection

2011 ◽  
pp. 181-200 ◽  
Author(s):  
Malek Ben Salem ◽  
Salvatore J. Stolfo
Keyword(s):  
Search Behavior ◽  
Masquerade Detection ◽  
User Search Behavior

Effects of visual complexity on user search behavior and satisfaction: an eye-tracking study of mobile news apps

2021 ◽  
Author(s):  
Fu Guo ◽  
Jiahao Chen ◽  
Mingming Li ◽  
Wei Lyu ◽  
Junjie Zhang
Keyword(s):  
Eye Tracking ◽  
Visual Complexity ◽  
Search Behavior ◽  
User Search Behavior

Towards Improving Web Search: A Large-Scale Exploratory Study of Selected Aspects of User Search Behavior

2009 ◽  
pp. 379-386 ◽  
Author(s):  
Hiroaki Ohshima ◽  
Adam Jatowt ◽  
Satoshi Oyama ◽  
Satoshi Nakamura ◽  
Katsumi Tanaka
Keyword(s):  
Exploratory Study ◽  
Large Scale ◽  
Web Search ◽  
Search Behavior ◽  
User Search Behavior

Analysis on the Digging of Social Network Based on User Search Behavior

2016 ◽  
Vol 10 (5) ◽  
pp. 297-304
Author(s):  
Junyi Du ◽  
Zhiyong Zhang ◽  
Changwei Zhao
Keyword(s):  
Social Network ◽  
Search Behavior ◽  
User Search Behavior

scholarly journals How Users Search the Library from a Single Search Box

2013 ◽  
Vol 74 (3) ◽  
pp. 227-241 ◽  
Author(s):  
Cory Lown ◽  
Tito Sierra ◽  
Josh Boyer
Keyword(s):  
Real World ◽  
Search Behavior ◽  
Public University ◽  
Real World Data ◽  
Library Search ◽  
Search Queries ◽  
Library Resources ◽  
User Search Behavior ◽  
Ongoing Evaluation ◽  
Library User

Academic libraries are turning increasingly to unified search solutions to simplify search and discovery of library resources. Unfortunately, very little research has been published on library user search behavior in single search box environments. This study examines how users search a large public university library using a prominent, single search box on the library website. The article examines two semesters of real-world data, totaling nearly 1.4 million transactions. Findings include that unified library search is about more than the catalog and articles, though these predominate. Additionally, a small number of the most popular search queries accounts for a disproportionate amount of the overall queries. Also discussed are the merits of ongoing evaluation of library user search behavior.

Personalized Spatio-Temporal OLAP Queries Suggestion Based on User Behavior and a New Similarity Measure

2019 ◽  
pp. 105-128
Author(s):  
Olfa Layouni ◽  
Jalel Akaichi
Keyword(s):  
Probabilistic Model ◽  
User Behavior ◽  
Search Behavior ◽  
Relevant Information ◽  
Current User ◽  
Data Cube ◽  
Temporal Data ◽  
Query Suggestion ◽  
Spatio Temporal ◽  
User Search Behavior

Spatio-temporal data warehouses store enormous amount of data. They are usually exploited by spatio-temporal OLAP systems to extract relevant information. For extracting interesting information, the current user launches spatio-temporal OLAP (ST-OLAP) queries to navigate within a geographic data cube (Geo-cube). Very often choosing which part of the Geo-cube to navigate further, and thus designing the forthcoming ST-OLAP query, is a difficult task. So, to help the current user refine his queries after launching in the geo-cube his current query, we need a ST-OLAP queries suggestion by exploiting a Geo-cube. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, first, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. Then, they propose a personalized query suggestion model based on users' search behavior, where they inject relevance between queries in the current session and current user' search behavior into a basic probabilistic model.

scholarly journals Predicting implicit search behaviors usinglog analysis

2018 ◽  
Vol 7 (1.7) ◽  
pp. 91
Author(s):  
L LeemaPriyadharshini ◽  
S Florence ◽  
K Prema ◽  
C Shyamala Kumari
Keyword(s):  
Web Search ◽  
User Behavior ◽  
Search Behavior ◽  
Web Page ◽  
User Behaviors ◽  
Needed Information ◽  
Efficiency And Effectiveness ◽  
Search Behaviors ◽  
User Search Behavior ◽  
The Web

Search engines provide ranked information based on the query given by the user. Understanding user search behavior is an important task for satisfaction of the users with the needed information. Understanding user search behaviors and recommending more information or more sites to the user is an emerging task. The work is based on the queries given by the user, the amount of time the user spending on the particular page, the number of clicks done by the user particular URL. These details will be available in the dataset of web search log. The web search log is nothing but the log which contains the user searching activities and other details like machine ID, browser ID, timestamp, query given by the user, URL accessed etc., four things considered as the important: 1) Extraction of tasks from the sequence of queries given by the user 2) suggesting some similar query to the user 3) ranking URLs based on the implicit user behaviors 4) increasing web page utilities based on the implicit behaviors. For increasing the web page utility and ranking the URLs predicting implicit user behavior is a needed task. For each of these four things designing and implementation of some algorithms and techniques are needed to increase the efficiency and effectiveness.

Clustering Users According to Common Interest Based on User Search Behavior

2010 ◽  
Vol 143-144 ◽  
pp. 851-855 ◽  
Author(s):  
Pei Ying Zhang ◽  
Ya Jun Du ◽  
Chang Wang
Keyword(s):  
Recommendation System ◽  
Search Behavior ◽  
Common Interest ◽  
Web Pages ◽  
User Session ◽  
Novel Method ◽  
The Common ◽  
Syntactic Similarity ◽  
User Search Behavior ◽  
The Web

The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.

scholarly journals Understanding PubMed(R) user search behavior through log analysis

Database ◽  
2009 ◽  
Vol 2009 (0) ◽  
pp. bap018-bap018 ◽  
Author(s):  
R. Islamaj Dogan ◽  
G. C. Murray ◽  
A. Neveol ◽  
Z. Lu
Keyword(s):  
Search Behavior ◽  
Log Analysis ◽  
User Search Behavior
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