Journal ArticleDOI
Recommendation system development for fashion retail e-commerce
Hyunwoo Hwangbo,Hyunwoo Hwangbo,Hyunwoo Hwangbo,Yang Sok Kim,Yang Sok Kim,Yang Sok Kim,Kyung Jin Cha,Kyung Jin Cha,Kyung Jin Cha +8 more
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TLDR
The experimental results show that the proposed K-RecSys system is superior in terms of product clicks and sales in the online shopping mall and its substitute recommendations are adopted more frequently than complementary recommendations.About:Â
This article is published in Electronic Commerce Research and Applications.The article was published on 2018-03-01. It has received 157 citations till now. The article focuses on the topics: Online and offline & Collaborative filtering.read more
Citations
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Journal ArticleDOI
An efficient recommendation generation using relevant Jaccard similarity
TL;DR: Two new simple but effective similarity models have been developed by considering all rating vectors of users to classify relevant neighborhoods and generate recommendations in a lower computation time by considering relevant Jaccard similarity.
Journal ArticleDOI
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
TL;DR: It was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field.
Journal ArticleDOI
Personalized digital marketing recommender engine
TL;DR: A model for delivering real-time, personalised marketing information concerning the recommended items for online and offline customers is described, using a blend of selling strategies: up-sell, cross-selling, best-in-class- selling, needs-satisfaction-selling and consultative-selling.
Journal ArticleDOI
A Systematic Study on the Recommender Systems in the E-Commerce
Pegah Malekpour Alamdari,Nima Jafari Navimipour,Mehdi Hosseinzadeh,Ali Asghar Safaei,Aso Mohammad Darwesh +4 more
TL;DR: A comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of e-commerce recommender systems to identify the gaps and significant issues of the RSs’ traditional methods, which guide the researchers to do future work.
Journal ArticleDOI
A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) approach for Recommendation Systems
Sunkuru Gopal Krishna Patro,Brojo Kishore Mishra,Sanjaya Kumar Panda,Raghvendra Kumar,Hoang Viet Long,David Taniar,Ishaani Priyadarshini +6 more
TL;DR: A Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features is proposed.
References
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Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.