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.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

BUILDING A KNOWLEDGE BASE FOR IMPLEMENTING A WEB-BASED COMPUTERIZED RECOMMENDATION SYSTEM

2007 ◽  
Vol 16 (05) ◽  
pp. 793-828 ◽  
Author(s):  
JUAN D. VELÁSQUEZ ◽  
VASILE PALADE
Keyword(s):  
Knowledge Base ◽  
Web Site ◽  
Web Mining ◽  
Recommendation System ◽  
The Internet ◽  
Web Pages ◽  
Web Based ◽  
Web Logs ◽  
Mining Tools ◽  
The Web

Understanding the web user browsing behaviour in order to adapt a web site to the needs of a particular user represents a key issue for many commercial companies that do their business over the Internet. This paper presents the implementation of a Knowledge Base (KB) for building web-based computerized recommender systems. The Knowledge Base consists of a Pattern Repository that contains patterns extracted from web logs and web pages, by applying various web mining tools, and a Rule Repository containing rules that describe the use of discovered patterns for building navigation or web site modification recommendations. The paper also focuses on testing the effectiveness of the proposed online and offline recommendations. An ample real-world experiment is carried out on a web site of a bank.

scholarly journals Webpage Recommendation System Based on the Social Media Semantic Details of the Website

2021 ◽  
Vol 12 (6) ◽  
pp. 237-243
Author(s):  
Dr. R.Rooba Et.al
Keyword(s):  
Recommendation System ◽  
Semantic Information ◽  
Markov Chain Model ◽  
Similarity Score ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
The Social ◽  
Recommendation Accuracy ◽  
The Web

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

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.

scholarly journals User search terms and controlled subject vocabularies in an institutional repository

2017 ◽  
Vol 35 (3) ◽  
pp. 360-367
Author(s):  
Scott Hanrath ◽  
Erik Radio
Keyword(s):  
Web Search ◽  
Search Behavior ◽  
Institutional Repository ◽  
Content Type ◽  
Search Queries ◽  
Search Terms ◽  
Content Search ◽  
Comprehensive Search ◽  
Novel Method ◽  
User Search Behavior

Purpose The purpose of this paper is to investigate the search behavior of institutional repository (IR) users in regard to subjects as a means of estimating the potential impact of applying a controlled subject vocabulary to an IR. Design/methodology/approach Google Analytics data were used to record cases where users arrived at an IR item page from an external web search and subsequently downloaded content. Search queries were compared against the Faceted Application of Subject Terminology (FAST) schema to determine the topical nature of the queries. Queries were also compared against the item’s metadata values for title and subject using approximate string matching to determine the alignment of the queries with current metadata values. Findings A substantial portion of successful user search queries to an IR appear to be topical in nature. User search queries matched values from FAST at a higher rate than existing subject metadata. Increased attention to subject description in IR records may provide an opportunity to improve the search visibility of the content. Research limitations/implications The study is limited to a particular IR. Data from Google Analytics does not provide comprehensive search query data. Originality/value The study presents a novel method for analyzing user search behavior to assist IR managers in determining whether to invest in applying controlled subject vocabularies to IR content.

scholarly journals Real and Promoted Aesthetic Properties of Geosites: New Empirical Evidence from SW Russia

Heritage ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 160-170
Author(s):  
Dmitry A. Ruban ◽  
Natalia N. Yashalova
Keyword(s):  
Physical Properties ◽  
Empirical Evidence ◽  
Web Pages ◽  
Significant Discrepancy ◽  
Natural Heritage ◽  
Aesthetic Properties ◽  
Adequate Representation ◽  
The Common ◽  
Practical Recommendations ◽  
The Web

Aesthetic properties of natural heritage objects are determined by their physical properties. Online promotion of these objects to potential tourists requires adequate representation of these properties on web pages. The Shum waterfall is a small, but notable and tourism-important geosite of southwestern Russia. Its real aesthetic properties were examined in the field, and 20 web pages devoted to local tourism were examined to judge its promoted aesthetic properties. Eleven criteria of the common tourists’ judgments of beauty were used for this purpose. A significant discrepancy between the real and promoted properties is found. Particularly, the web pages exaggerate the scale of the waterfall and do not mention crowds of tourists. This may cause disappointment of the latter. The findings of the present study allow for making several practical recommendations for more efficient promotion of the Shum waterfall, as well as providing general advice to the geotourism industry.

An Improved Web Page Recommendation Technique for Better Surfing Experience

2018 ◽  
Vol 8 (4) ◽  
pp. 1-13
Author(s):  
Rajnikant Bhagwan Wagh ◽  
Jayantrao Bhaurao Patil
Keyword(s):  
Web Site ◽  
Recommendation System ◽  
Information Overload ◽  
Experimental Results ◽  
Relationship Matrix ◽  
Web Pages ◽  
Threshold Values ◽  
Web Page ◽  
Clustering And Classification ◽  
The Web

Recommendation systems are growing very rapidly. While surfing, users frequently miss the goal of their search and lost in information overload problem. To overcome this information overload problem, the authors have proposed a novel web page recommendation system to save surfing time of user. The users are analyzed when they surf through a particular web site. Authors have used relationship matrix and frequency matrix for effectively finding the connectivity among the web pages of similar users. These webpages are divided into various clusters using enhanced graph based partitioning concept. Authors classify active users more accurately to found clusters. Threshold values are used in both clustering and classification stages for more appropriate results. Experimental results show that authors get around 61% accuracy, 37% coverage and 46% F1 measure. It helps in improved surfing experience of users.

How to Promote Community Portals

2011 ◽  
pp. 454-460
Author(s):  
Aki Vainio ◽  
Kimmo Salmenjoki
Keyword(s):  
Information Content ◽  
Search Engines ◽  
Web Pages ◽  
Web Environment ◽  
The Common ◽  
Marketing Tool ◽  
The Masses ◽  
The Web

Information content of the Web has, in the last 10 years, changed from informative to communicative. Web pages, especially homepages, were the foremost places where companies, organizations, and individuals alike expressed their existence online and provided some information about themselves, like their products, services, or artefacts that they related to. On the common Web environment, the search engines were harvesting this information and made it available and meaningful for the masses of Web users. In the early days of Web, this factor alone justified the usage of Web as a marketing tool and as an easy way to share important information between collaborating partners.

Web Page Recommendation System Using Laplace Correction Dependent Probability and Chronological Dragonfly-Based Clustering

2018 ◽  
Vol 7 (3.27) ◽  
pp. 290
Author(s):  
Jyoti Narayan Jadhav ◽  
B Arunkumar
Keyword(s):  
Recommendation System ◽  
Pattern Mining ◽  
Web Pages ◽  
Web Page ◽  
Dragonfly Algorithm ◽  
History Of ◽  
Search History ◽  
Dependent Probability ◽  
F Measure ◽  
The Web

The web page recommenders predict and recommend the web pages to the users based on the behavior of their search history. The web page recommender system analyzes the semantics of the navigation by the user and predicts the related web pages for the user. Various recommender systems have been developed in the literature for the web page recommendation. In the first work, a web page recommendation system was developed using weighted sequential pattern mining and Wu and Li Index Fuzzy clustering (WLI-FC) algorithm. In this work, the Chronological based Dragonfly Algorithm (Chronological-DA) is proposed for recommending the webpage to the users. The proposed Chronological-DA algorithm includes the concept of the chronological for recommending the webpage based on the history of pages visited by the users. Also, the proposed recommendation system uses the concept of Laplacian correction for defining the recommendation probability. Simulation of the proposed webpage recommendation system with the chronological-DA uses the standard CTI and the MSNBC database for the experimentation, and the experimental results prove that the proposed scheme has better values of 1, 0.964, and 0.973 for precision, recall, and F-measure respectively.  

scholarly journals Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1918 ◽  
Author(s):  
Ruyan Wang ◽  
Yuzhe Liu ◽  
Puning Zhang ◽  
Xuefang Li ◽  
Xuyuan Kang
Keyword(s):  
Real Time ◽  
Information Search ◽  
Recommendation System ◽  
Search Behavior ◽  
User Interest ◽  
State Information ◽  
The Real ◽  
Clustering Model ◽  
Entity Identification ◽  
User Search Behavior

There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user’s potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.

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