Recommender systems in e-commerce

By Sanjeevan Sivapalan, A. Sadeghian, H. Rahnama, A. Madni. 2014

696 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Information Systems

In Recommender Systems

Citations in the corpus

Recurrent Latent Variable Networks for Session-Based Recommendation (2017, background): “Specifically, under a session-based setup, recommendation is based only on the actions of a user during a specific browsing session [29].”
Improved Recurrent Neural Networks for Session-based Recommendations (2016, background): “An alternative to relying on historical data is to make session-based recommendations [23].”
Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “The idea of price sensitivity in recommender systems for e-commerce has been mentioned as a potential direction in a classic survey [34], though surprisingly we find that this factor has received relatively little attention.”
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games (2017, background): “les include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative ﬁltering recommenders [15] predicting user interests through the wisdom of the crowd [16]. A next grand challenge of AGI i”
Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications (2015, background): “Schafer et al. 6 stated that the RS could produce an indication of the price sensitivity of the customer for a given product.”
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data (2017, methodology): “In order to utilize drug-drug similarities, we consider two representative methods [28, 32]: the neighbor recommender method and random walk method, and build DDI prediction models.”
Neural Attentive Session-based Recommendation (2017, background): “To tackle this problem, session-based recommendation [33] is proposed to predict the next item that the user is probably interested in based merely on implicit feedbacks, i.e., user clicks, in the current session.”
Recommender Systems (2016, neutral): “The broader applicability of recommender systems to e-commerce applications is discussed in [530].”
Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017, background): “Typical examples include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative filtering recommenders [15] predicting user interests through the wisdom of the crowd [16].”
A Time-Aware Exploration of RecSys15 Challenge Dataset (2016, background): “E-commerce is one of the main applications of recommender systems [9].”
Artificial intelligence in recommender systems (2020, background): “This area of research has been the focus of great concern for the past twenty years in both academia and industry, and research in this ﬁeld is often motivated by the potential proﬁt that recommender systems can generate for businesses such as Amazon [3].”
Collaborative filtering recommender systems (2013, methodology): “Recommendation methods have a variety of possible categories (Resnick and Varian, 1997; Schafer et al ., 1999).”
Style in the long tail: discovering unique interests with latent variable models in large scale social E-commerce (2014, background): “Sites like Ebay or Amazon surface personalized content to their users [19] by utilizing a wide range of recommendation system technologies.”
Recommendation Systems in Software Engineering (2014, methodology): “• WHERE is a version of PDDP that uses the FASTMAP trick to find an approximation to the first component [40].”
Online Clustering of Bandits (2014, background): “Examples of the second approach are the more traditional collaborative-filtering (e.g., (Schafer et al., 1999)), content-based filtering, and hybrid approaches (e.g. (Burke, 2005)).”