This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes
guidelines on how information systems (IS) research could contribute to this research …

Recommendation systems have emerged as a response to overload in terms of increased
amounts of information online, which has become a problem for users regarding the time …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

User-based collaborative filtering, a widely used nearest neighbour-based recommendation
technique, predicts an item's rating by aggregating its ratings from similar users. User …

Purpose This paper aims to identify, evaluate and integrate the findings of all relevant and
high-quality individual studies addressing one or more research questions about …

Abstract Recommender Systems (RSs) are powerful and popular tools for e-commerce. To
build their recommendations, RSs make use of varied data sources, which capture the …

Recommender systems are a valuable means for online users to find items of interest in
situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a …

Recommender systems are widely used to provide e-commerce users appropriate items.
Collaborative filtering is one of the most successful recommender approaches which …

Dropping fractions of users or items judiciously can reduce the computational cost of
Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time …

This paper presents the state of art techniques in recommender systems (RS). The various
techniques are diagrammatically illustrated which on one hand helps a naïve researcher in …