Journal ArticleDOI
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis
TLDR
It is contended that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.About:
This article is published in Electronic Commerce Research and Applications.The article was published on 2012-07-01. It has received 200 citations till now. The article focuses on the topics: Recommender system & Collaborative filtering.read more
Citations
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Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Posted Content
A Survey on Session-based Recommender Systems
TL;DR: A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.
Journal ArticleDOI
A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship
TL;DR: This study proposes a social recommender system that can generate personalized product recommendations based on preference similarity, recommendation trust, and social relations that outperforms other benchmark methodologies in terms of recommendation accuracy.
Journal ArticleDOI
A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining
TL;DR: The proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.
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
TL;DR: 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.
References
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Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
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.
Journal ArticleDOI
Using collaborative filtering to weave an information tapestry
TL;DR: Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
Proceedings ArticleDOI
Social information filtering: algorithms for automating “word of mouth”
Upendra Shardanand,Pattie Maes +1 more
TL;DR: The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.