Journal ArticleDOI
E-Commerce Recommendation Applications
Reads0
Chats0
TLDR
An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.Abstract:
i>Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.read more
Citations
More filters
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.
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.
Journal ArticleDOI
Amazon.com recommendations: item-to-item collaborative filtering
TL;DR: Item-to-item collaborative filtering (ITF) as mentioned in this paper is a popular recommendation algorithm for e-commerce Web sites that scales independently of the number of customers and number of items in the product catalog.
Journal ArticleDOI
Link prediction in complex networks: A survey
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
Book ChapterDOI
Introduction to Recommender Systems Handbook
TL;DR: The main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
References
More filters
Proceedings ArticleDOI
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system
TL;DR: The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques and is experimentally validated by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering system.
Journal ArticleDOI
The Market for Evaluations
TL;DR: In this paper, the authors present pricing and subsidy mechanisms that operate through a computerized market and induce the efficient provision of product evaluations, and overcome three major challenges: evaluations, which are public goods, are likely to be underprovided; an inefficient ordering of evaluators may arise; and the optimal quantity of evaluations depends on what is learned from the initial evaluations.
Proceedings ArticleDOI
Statistics and data mining techniques for lifetime value modeling
TL;DR: Using the proportional hazards and neural network models in tandem, it is demonstrated how data mining tools can be apt complements of the classical statistical models, and show that their combined usage overcomes many of the shortcomings of each separate tool setresulting in a LTV tenure prediction model that is both accurate and understandable.