Dynamic Recommendation System Using Web Usage Mining for E-commerce Users

Lopes, Prajyoti; Roy, Bidisha

doi:10.1016/j.procs.2015.03.086

Cited by 52 publications

(22 citation statements)

References 10 publications

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“…Another approach for Web personalization relies on Web usage mining [40][41][42][43]. This approach is increasingly becoming popular among researchers and businesses alike, as it allows them to discover the users' browsing behavior.…”

Section: Web and Cloud Services Personalizationmentioning

confidence: 99%

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

et al. 2018

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Cloud service providers typically compose their services from a number of elementary services, which are developed in-house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user's mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context criteria that combine cloudservice provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

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Section: Web and Cloud Services Personalizationmentioning

confidence: 99%

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

et al. 2018

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“…It uses the Bayesian Network to capture relationship between user and item relation. Matrix factorization is another important technique that is first to point the QOS (Quality-ofservice) prediction problem [22].…”

Section: A11 Model Based Collaborative Filteringmentioning

confidence: 99%

Survey on Personalized Web Recommender System

Kumar¹,

Varsha²

2018

IJIEEB

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Recommendation system plays an essential role in searching any information from World Wide Web. Recommender system handles Information straining problem and improve customer correlation by providing best services. It suggests items or services to users according to their interest, navigation behavior or demographic information. This paper performs a survey on different approaches available for recommender system and performs a comparative analysis of different algorithms. Further, a discussion about various application areas has been done. At the end, issues and challenges in recommender systems have been discussed.

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“…Rajhans Mishra et al [4] have implemented the algorithm proposed by Pradeep Kumar having similarity measures for finding the clusters and outliers. Four similarity measures have been used that are Jaccard, Dice, S 3 M and Levenshtein.…”

Section: IImentioning

confidence: 99%

A Review on: Designing a Recommender System Using Sequential Logs

Patel¹,

Vishwakarma²

2017

IJERT

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Abstract-Recommender system has change the way of searching for information over the internet. The information over the internet is overburden. The searching for specific information sometimes becomes very time consuming. Today in the fast growing world no one wants to wait everyone wants o work fast. This in result has increased the competition for achieving success. So it is very necessary to save time in searching information. Recommender system is solution for the problem by providing recommendation to the users. This paper has discussed about the types of recommender system like collaborative filtering, content based filtering and hybrid filtering along with the challenges of recommendation system. The paper has also discussed about the sequential information of user behavior which is stored in the web servers. In this paper only the idea is being discussed.

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Dynamic Recommendation System Using Web Usage Mining for E-commerce Users

Cited by 52 publications

References 10 publications

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

Survey on Personalized Web Recommender System

A Review on: Designing a Recommender System Using Sequential Logs

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