A Survey of Recommendation Algorithms
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6
Abstract
Abstract: Recommender system can be defined as the subclass of information filtering system which attemptsto give the guidance to the users regarding the useful services based on their personalized preferences, pastbehavior or based on their similar likings with other users. The various approaches of recommendation systems,like content-based, collaborative filtering, hybrid, etc, can further be classified according to their algorithmictechnique as memory-based (heuristic) or model-based recommendation algorithms. Service recommendersystems provides appropriate recommendations of services like movies, hotels, gadgets, etc, leading to anincrease in the amount of data on the web, known as Big Data. It is becoming difficult to capture, store, manageand analyze such big data that affects the service recommender systems with issues like scalability andinefficiency. Also many existing service recommender system provides the same recommendations to differentusers based on ratings and rankings only, without considering the taste and preference of an individual user.This paper presents a survey on various recommendation algorithms, elaborating all its types along with itsdrawbacks. The paper also focuses on the solutions to overcome these drawbacks and provide aptrecommendations to the users. It also deals with the solution to provide apt recommendations of the services tothe users in big data environment. The issues of scalability and inefficiency while managing big data can besolved by using a distributed computing platform known as Hadoop.
Authors and Affiliations
Ruchita V. Tatiya , Prof. Archana S. Vaidya
Keywords
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