The Big Promise of Recommender Systems

The Big Promise of Recommender Systems

suggesting interesting things to its users after learning their preferences over time (Jannach et al. 2010, Ricci et al. 2011). Recommender systems were envisioned in the 1970s (Negroponte 1970), conceptualized and prototyped in the early 1990s (Goldberg et al. 1992), and implemented and first commercialized in the mid-1990s (Resnick and Varian 1997). They have two (sometimes diametrically oppo...

Big Data and Recommender Systems

Context-Aware Recommender Systems: A Review of the Structure Research

 Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...

Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...

Delivering on the Promise of Big Data and the Cloud

We are living in an age inundated with information. Our world is increasingly instrumented—sensors are collecting data on everything from hospital patients’ vital signs, to the moment-by-moment navigation of commercial aircraft, to consumer behavior based on buying patterns and the use of membership cards. Waves of data are coming from social media sites, from radio-frequency tracking systems, ...