Using log mining to analyze user behavior on search engine

2011 ◽  
Author(s):  
Ke Xie ◽  
Huijia Yu ◽  
Rongwei Cen
Keyword(s):  
Search Engine ◽  
User Behavior ◽  
Log Mining

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

A New Replacement Algorithm of Web Search Engine Cache based on User Behavior

2014 ◽  
Vol 8 (6) ◽  
pp. 3049-3054
Author(s):  
Zhang Yong-Heng ◽  
Zhang Feng ◽  
You Fei
Keyword(s):  
Search Engine ◽  
Web Search ◽  
User Behavior ◽  
Replacement Algorithm ◽  
Web Search Engine

Automatic Search Engine Performance Evaluation Based on User Behavior Analysis

2009 ◽  
Vol 19 (11) ◽  
pp. 3023-3032 ◽  
Author(s):  
Yi-Qun LIU ◽  
Rong-Wei CEN ◽  
Min ZHANG ◽  
Li-Yun RU ◽  
Shao-Ping MA
Keyword(s):  
Performance Evaluation ◽  
Behavior Analysis ◽  
Search Engine ◽  
User Behavior ◽  
Engine Performance ◽  
User Behavior Analysis ◽  
Automatic Search

From Analysis to Estimation of User Behavior

2011 ◽  
pp. 206-226 ◽  
Author(s):  
Seda Ozmutlu ◽  
Huseyin C. Ozmutlu ◽  
Amanda Spink
Keyword(s):  
Search Engine ◽  
Language Processing ◽  
Preliminary Analysis ◽  
User Behavior ◽  
User Profiling ◽  
Log Analysis ◽  
Information Searching ◽  
Simple Task ◽  
User Behavior Analysis ◽  
Correct Estimation

This chapter summarizes the progress of search engine user behavior analysis from search engine transaction log analysis to estimation of user behavior. Correct estimation of user information searching behavior paves the way to more successful and even personalized search engines. However, estimation of user behavior is not a simple task. It closely relates to natural language processing and human computer interaction, and requires preliminary analysis of user behavior and careful user profiling. This chapter details the studies performed on analysis and estimation of search engine user behavior, and surveys analytical methods that have been and can be used, and the challenges and research opportunities related to search engine user behavior or transaction log query analysis and estimation.

scholarly journals The LAILAPS Search Engine: A Feature Model for Relevance Ranking in Life Science Databases

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Christian Colmsee ◽  
Steffen Flemming ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Search Engine ◽  
Life Science ◽  
User Behavior ◽  
Science Research ◽  
Feature Model ◽  
Relevance Ranking ◽  
Great Chance ◽  
Regression Problem ◽  
Prediction Function ◽  
Search Engine Result

SummaryEfficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking.In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable.The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries.Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

scholarly journals ONLINE LOG ANALYSIS FOR DETECTING USER BEHAVIOR

2015 ◽  
pp. 270-275
Author(s):  
ZINGADE A.M. ◽  
P.P. KALYANKAR
Keyword(s):  
Operating System ◽  
Search Engine ◽  
Search Engines ◽  
User Behavior ◽  
Block Size ◽  
Log Analysis

Most search engines are not provided the facility of expected results every time. Because two words can have same meaning e.g. if query “block size” is entered in search engine then search engine will display the records related to operating system as well as the living apartment block size. But search engine don‘t know about what actually user expect. That is data related to operating system or data related to living apartment. So generally it will display both type of links In this paper we are managing the search engines on the fly. That is the paper is related to personalize the search of user on the fly without asking users to tagging or rating for the page.

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