A web recommendation system considering sequential information

Mishra, Rajhans; Kumar, Pradeep; Bhasker, Bharat

doi:10.1016/j.dss.2015.04.004

Cited by 98 publications

(49 citation statements)

References 27 publications

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“…In recent years, it has been widely used in e-commerce sites to provide consumers with product purchasing advice [17,18]. Since the development of the first recommendation system by Goldberg and his colleagues [19], various recommendation systems and related technologies have been introduced.…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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“…Rajhans Mishra et al [1] have proposed a model to predict user's next page visit. The next page visit of a user will provide the useful information about their likes and interest.…”

Section: Literature Reviewmentioning

confidence: 99%

A Web Recommender System using User Logs Files with k-NN Method

Patel¹,

Vishwakarma²

2017

IJCA

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In the current era there is a huge increment in the generation of web data. Internet is getting overloaded with the massive increase in data. This unstable growth in information is making the search a complicate process. This in result has given rise to a new idea to analysis the system. Web recommender system is the most efficient solution for the problem and is widely used in e-commerce websites to suggest product in reference to the user request. Giving suggestion is not an easy task, whereas it not only helps in saving time but also helps in decision making. Web servers contain log files these log files have records of events in the sequential pattern. Sequential information gives the detail information about the user's behavior. In this paper k-NN method is implemented to obtain the prediction for the new users. The dataset used in this paper is the dummy dataset. General TermsE-commerce websites, log files, dummy dataset.

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“…So far many research works have been done in the field of web recommender system. Some of the studies related to web recommender system are discussed below Rajhans Mishra et al [1] have proposed a model to predict a user's next page visit. The next page visit of a user will provide the useful information about their likes and interest.…”

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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A web recommendation system considering sequential information

Cited by 98 publications

References 27 publications

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

A Web Recommender System using User Logs Files with k-NN Method

A Review on: Designing a Recommender System Using Sequential Logs

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