Dynamic personalization of web sites without user intervention

Baraglia, Ranieri; Silvestri, Fabrizio

doi:10.1145/1216016.1216022

Cited by 83 publications

(47 citation statements)

References 8 publications

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“…Our survey reveals that there is a race for finding architecture concept (Baraglia and Silversti, 2007) that helps to improve the accuracy of online recommendation system in capturing imminent navigation pattern of online users. In our study, we proposed the novel architecture for online recommendation system that uses the novel user navigation classification approach and website ontology concept scoring algorithm for improving accuracy.…”

Section: Methodsmentioning

confidence: 99%

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An Ontology Based Approach to Implement the Online Recommendation System

Mohanraj¹

2011

Journal of Computer Science

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Problem statement: Every web user has different intent when accessing the information on website. The primary goal of recommendation system is to anticipate the user intent and recommend the web pages that contain user expected information. Effective recommendation of web pages involves two important challenges: accurately identifying the user intent and predict the result show that novel web usage mining method and ontological concept scoring algorithm based on website domain ontological profile helps the recommendation system imminent navigation pattern in such a way that it provides required content while users browse the predicted navigation. Approach: We present a ontology based approach to implement recommendation system that involves applying innovative web usage mining on log system to discover all possible imminent navigation patterns of current user and resolve any uncertainties in discovering the navigation pattern by applying ontological concept based similarity comparison and scoring algorithm. Results: result show that novel web usage mining method and ontological concept scoring algorithm based on website domain ontological profile helps the recommendation system to predict and present most relevant navigation pattern to users. Conclusion: our recommendation system confirms that ontology based approach should be used to ensure excellent accuracy in predicting and capturing future navigation pattern of web user.

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Section: Methodsmentioning

confidence: 99%

“…Another WUM system called SUGGEST (Baraglia Silversti, 2007) provides useful information to optimize web server performance and make easier for the web user navigation. SUGGEST adopts a two level architecture composed by an offline creation of historical knowledge and online component that understands the user's behaviour.…”

Section: Introductionmentioning

confidence: 99%

An Ontology Based Approach to Implement the Online Recommendation System

Mohanraj¹

2011

Journal of Computer Science

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“…Regarding user modeling, such parameter represents an aspect of a user future behavior, likes or preferences. The usage of statistical and probabilistic user models has been manifested by the increasing publication of research in these lines [8,50]. However, for several years it is claimed that those approaches are still far of being applicable and effective in some fields [51], and therefore there is still room for research in this area.…”

Section: State Of the Art In User Modelingmentioning

confidence: 99%

Statistical user model supported by R-Tree structure

et al. 2013

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This paper is about developing a group user model able to predict unknown features (attributes, preferences, or behaviors) of any interlocutor. Specifically, for systems where there are features that cannot be modeled by a domain expert within the human computer interaction. In such cases, statistical models are applied instead of stereotype user models. The time consumption of these models is high, and when a requisite of bounded response time is added most common solution involves summarizing knowledge. Summarization involves deleting knowledge from the knowledge base and probably losing accuracy in the medium-term. This proposal provides all the advantages of statistical user models and avoids knowledge loss by using an R-Tree structure and various search spaces (universes of users) of diverse granularity for solving inferences with enhanced success rates. Along with the formalization and evaluation of the approach, main advantages will be discussed, and a perspective for its future evolution is provided. In addition, this paper provides a framework to evaluate statistical user models and to enable performance comparison among different statistical user models.

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“…Baraglia and Palmerini proposed a WUM system called SUGGEST that provide useful information to make the web user navigation easier and to optimize the web server performance [4]. SUGGEST adopts a two level architecture composed by an offline creation of historical knowledge and an online engine that understands user's behavior.…”

Section: Background and Related Workmentioning

confidence: 99%

A Personalized Product Based Recommendation System Using Web Usage Mining and Semantic Web

S¹,

Mahadevan²,

Prakash³

2012

IJCTE

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Abstract-To be globally competent and competitive a successful presence on the web is necessary to sustain and retain itself in the market. The WWW is an interesting area for data mining because of abundance of information. Web users exhibit a variety of navigational interests through clicking a sequence of web pages. Analysis of this data will lead to discover many interesting patterns and facilitate users to locate more preferable web pages. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. The semantic information of the Web page contents is generally not included in Web usage mining. Online recommendation and prediction is one of the web usage mining applications. In this paper we present architecture for integrating semantic information about the products with web log data and generate a list of recommended products by using LCS Algorithm.

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Dynamic personalization of web sites without user intervention

Abstract: A novel online recommender system builds profiling models and offers suggestions without the user taking the lead.

Cited by 83 publications

References 8 publications

An Ontology Based Approach to Implement the Online Recommendation System

An Ontology Based Approach to Implement the Online Recommendation System

Statistical user model supported by R-Tree structure

A Personalized Product Based Recommendation System Using Web Usage Mining and Semantic Web

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