A knowledge-based product recommendation system for e-commerce

Prasad, Bhanu

doi:10.1504/ijiids.2007.013283

Cited by 17 publications

(14 citation statements)

References 27 publications

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“…For example, Prasad [8] presented a knowledge-based product recommendation system, RecommendEx, for e-commerce purposes. The system used case-based reasoning plan recognition approaches and automated collaborative filtering approaches in its applications.…”

Section: ) Knowledge-based (Kb)mentioning

confidence: 99%

“…These systems, especially the k-nearest neighbor collaborative filtering (CF)-based approaches, are achieving widespread success on the Web [7]. Various recommender systems have been extensively applied in ecommerce and e-business [8,9], but their adoption in the context of e-government has received less attention. This study presents a recommender system design, named Intelligent Business Partner Locator (IBPL), which aims to help government effectively recommend the right business partners (e.g., international buyers, agents, distributors, and retailers) to individual businesses (e.g., exporters) based on their requirements, interests and business product categories.…”

Section: Introductionmentioning

confidence: 99%

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Recommendation technique-based government-to-business personalized e-services

Shambour

Zhang

2009

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society

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One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy setsbased semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners.

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Section: ) Knowledge-based (Kb)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommendation technique-based government-to-business personalized e-services

Shambour

Zhang

2009

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society

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“…It calculates the probabilities of inter-dependent events, then calculate initialized probability and finally revised probability of the events. This paper also outlines the implementation of Bayesian Belief Network (BBN) by applying weights [7,8,9,10] to increase the product sales.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Belief Networks – Based Product Prediction for E-Commerce Recommendation

Thakur

Kundu

Sing

2011

Computer Networks and Information Technologies

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Abstract. Prediction systems apply knowledge discovery techniques to the problem of making personalized product recommendations. New recommender system technologies are needed that can quickly produce quality recommendations, even for very large-scale problems. This paper presents a new and efficient approach that works using Bayesian belief networks (BBN) and that calculate the probabilities of inter-dependent events by giving each parent event a weighting (Expert systems). To get best result for the sales prediction, different weights have been applied on the proposed algorithm. Finally we got results for prediction, for a given product, using our proposed algorithm and final recommendation [1] has been made to the customers based on preference similarity.

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“…They are Automated Collaborative Filtering (ACF) and Case-Based Reasoning [2]. The first one, ACF [1] is widely used as the technique for product recommendation in an online store. This approach is based on the feedback given by the previous customers and their feedback is used to recommend the product to new customer.…”

Section: Introductionmentioning

confidence: 99%

“…This paper utilizes the vector space model rather than mean squared difference formula. ACF approaches can be classified as non-invasive and invasive approaches, based on how the user's preferences are recorded in an ACF system [1]. In invasive approach, user's ratings are floating numbers between 0 and 1.…”

Section: Introductionmentioning

confidence: 99%

A Product Recommendation System Using Vector Space Model and Association Rule

Mukhopadhyay

Dutta²,

Kundu³

et al. 2008

2008 International Conference on Information Technology

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This paper presents an alternative product recommendation system for Business-to-customer ecommerce purposes. The system recommends the products to a new user. It depends on the purchase pattern of previous users whose purchase pattern are close to that of new user. The system is based on vector space model to find out the closest user profile among the profiles of all users in database. It also implements Association rule mining based recommendation system, taking into consideration the order of purchase, in recommending more than one product. To make the association rule memoryefficient, cellular automata is used.

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A knowledge-based product recommendation system for e-commerce

Cited by 17 publications

References 27 publications

Recommendation technique-based government-to-business personalized e-services

Recommendation technique-based government-to-business personalized e-services

Bayesian Belief Networks – Based Product Prediction for E-Commerce Recommendation

A Product Recommendation System Using Vector Space Model and Association Rule

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