scholarly journals User profiling for web personalization using multi agent and DBSCAN based approach

2018 ◽  
Vol 7 (2) ◽  
pp. 849
Author(s):  
Sipra Sahoo ◽  
Bikram Kesari Ratha
Keyword(s):  
User Profile ◽  
Storage Management ◽  
User Profiling ◽  
Web Pages ◽  
Multi Agent System ◽  
User Profiles ◽  
Web Page ◽  
Web Personalization ◽  
Reference Ontology ◽  
Multi Agent

The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.

Feature weighted clustering for user profiling

2017 ◽  
Vol 08 (04) ◽  
pp. 1750056 ◽  
Author(s):  
Ayse Cufoglu ◽  
Mahi Lohi ◽  
Colin Everiss
Keyword(s):  
Clustering Algorithm ◽  
Personal Information ◽  
Clustering Algorithms ◽  
User Profile ◽  
User Profiling ◽  
User Profiles ◽  
Related Information ◽  
Weighted Clustering ◽  
Feature Weight ◽  
Feature Values

Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.

scholarly journals Enhancing User Profile Accuracy for Personalized Web Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 769-774
Keyword(s):  
Web Applications ◽  
User Profile ◽  
User Profiles ◽  
The Real ◽  
Concept Hierarchy ◽  
Reference Ontology ◽  
Customized Products ◽  
Profile Accuracy ◽  
Web Systems

Personalized Web Applications aim to improve the user's browsing experience by offering customized products and services based on his preferences and needs. A key feature of a successful personalization system is building profiles that accurately express the real interests and needs of each user. In this work, we focus on creating accurate, complete and dynamic profiles by capturing and tracking the users’ browsing activities. Moreover, we implement techniques to increase the accuracy of the retrieved user profiles by collecting more browsing data, identifying the most important concepts and removing irrelevant ones, and the number of levels from the concept hierarchy in the reference ontology that we should use to efficiently represent the users’ reel interests and needs. The result is a complete, dynamic, and accurate user profile that can be used to give users better-personalized browsing experience.

scholarly journals Trajectory Based Location Prediction and Enriched Ontological User Profiles for Efficient Website Recommendation

2020 ◽  
Vol 9 (3) ◽  
pp. 250-257
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
User Profile ◽  
User Profiling ◽  
Location Prediction ◽  
Semantic Features ◽  
User Profiles ◽  
Search Results ◽  
Dynamic Location ◽  
Prediction Approach

The spread over of huge amount of information in the vast area of internet makes difficult for the users to obtain the search items that are relevant to them. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfill their need, it is necessary to know their preferences of search at various contexts. In general, the user profiles are used to determine the taste of the users. The traditional method of user profiling does not provide a complete detail regarding their search. In addition, the search preference of the individuals varies in accordance with time and location. The user profiles do not update the dynamic location changes of the users. The traditional location based recommendation systems suggest the search results based on their location to compensate the dynamic preferences of the users. The drawbacks of the conventional systems are resolved by the Location and User Profile (LUP) based recommendation system. To attain a higher user satisfaction by providing accurate search results, a trajectory based location prediction and enriched ontological user profiles to recommend the appropriate websites to the users is proposed in this paper. In this article, we suggest a novel method for predicting the location of a user's profile using Semantic Trajectory Pattern (STP), based on both the place and semantic features of user trajectories. Our prediction model 's central concept is based on a novel cluster-based prediction approach that evaluates the location of user search data based on the regular activities of related users in the same cluster, calculated by evaluating the typical behavior of users in semantic trajectories. The combination of location information along with enriched ontological user profiles improves the efficiency of the proposed web recommendation system. The experimental results are evaluated using recall, precision and F-measure metrics.

Intelligent Information Retrieval Using Fuzzy Association Rule Classifier

2012 ◽  
pp. 159-172
Author(s):  
Sankaradass Veeramalai ◽  
Arputharaj Kannan
Keyword(s):  
Information Retrieval ◽  
Association Rule ◽  
Web Applications ◽  
Web Pages ◽  
User Profiles ◽  
Web Page ◽  
Filtering Method ◽  
Intelligent Information Retrieval ◽  
Fuzzy Association Rule ◽  
Intelligent Information

As the use of web applications increases, when users use search engines for finding some information by inputting keywords, the number of web pages that match the information increases at a tremendous rate. It is not easy for a user to retrieve the exact web page which contains information he or she requires. In this paper, an approach to web page retrieval system using the hybrid combination of context based and collaborative filtering method employing the concept of fuzzy association rule classification is introduced and the authors propose an innovative clustering of user profiles in order to reduce the filtering space and achieves sub-linear filtering time. This approach can produce recommended web page links for users based on the information that associates strongly with users’ queries quickly with better efficiency and therefore improve the recall, precision of a search engine.

Research on Intelligent ERP System Based on Multi-Agent

2014 ◽  
Vol 644-650 ◽  
pp. 6157-6160
Author(s):  
Hong Wei Lv
Keyword(s):  
Control Agent ◽  
Storage Management ◽  
System Model ◽  
Agent Technology ◽  
Multi Agent System ◽  
System Framework ◽  
Erp System ◽  
Development Framework ◽  
Multi Agent ◽  
Conducting Research

The realization of intelligent ERP system is studied through Agent technology and Jadex software development framework. The concept of intelligent ERP system is proposed in this paper based on Multi-Agent technology. After conducting research on Multi-Agent ERP system model frame, the Multi-Agent ERP model is designed based BDI model. Multi-Agent ERP system model based on Multi-Agent system framework and layers are also given. Finally, take storage management system as an example, storage control Agent is designed and realized on Jadex.

Web Application for User Profiling

2016 ◽  
Vol 8 (2) ◽  
pp. 44-56
Author(s):  
Taous Iggui ◽  
Hassina Nacer ◽  
Youcef Sklab ◽  
Taklit Ait Radi
Keyword(s):  
Natural Language Processing ◽  
Information Extraction ◽  
Language Processing ◽  
Web Application ◽  
User Profile ◽  
User Profiling ◽  
Good Precision ◽  
User Profiles ◽  
Extraction Techniques ◽  
Novel Approach

User's profiles play an important role when information systems try to meet their needs. This work presents a novel approach to build user profiles. It is based on information extraction techniques and proceeds by iterative steps. The use of different statistic metrics, Natural Language Processing (NLP) techniques and semantic descriptions (ontologies) in the authors' approach, has provided it with a good precision degree when extracting information from texts. This has been demonstrated by an application prototype which is an automatic user profile constructor, using the texts of emails job applications (E recruitment field).

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