An efficient and scalable recommender system for the smart web

Abstract

This is a postprint version of the following published document: Abstract: This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two differ-ent working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and imple-mented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.

Recommender system introduction for requests of cancer world

“…44 A web-based recommender system is a web-based software that offers web pages based on the interests and needs of the users. 18,[60][61][62][63][64][65][66] When a recommender system is placed on a website or a social network, it helps the users to refine a huge amount of information and recommends appropriate information according to the needs of the users; also, it provides the possibility of predicting the future requests of the users. 63,66,67 Web-based recommender systems search the web using data mining techniques and evaluate it in terms of content, structure, and utility, obtain the data patterns, and make recommendations and predictions with the help of their refiner techniques.…”