Review of Techniques for Recommender Systems

Abstract

Abstract-In electronic commerce, recommender systems are used to help customers to choose products according to their needs. These systems suggest products automatically to users by learning their requirements. Recommendations provided by these systems depends upon users purchase probability and preferences. In this paper, different techniques used for recommender systems are studied.Keywords-Recommendation Systems, E-commerce, Content Based Filtering, Collaborative Filtering, Hybrid Methods I. INTRODUCTIONRecommender systems touch our lives every day, from searching on Google to shopping on at any major online retailer. Their sophisticated algorithms attempt and often succeed at showing us the information and products we seek. The information consumers give to service providers and retailers is expanding rapidly, which makes recommendation systems both more complex, and potentially more powerful. Online behaviour including customer metadata, transaction histories and communications allows companies to understand shoppers better, sense their similarities, and address needs they may not even know they have. Simultaneously, we are better able to analyse information about the items sought, including images, sounds in the case of music, and descriptions, which allows companies to refine the ways they cluster products, as well as whom they target as potential buyers. Recommendation systems are at the center of retail both online and off, showing ads on web sites as well as through dynamic displays in brick and mortar stores. By using facial recognition and in-store video cameras, retailers are able to group their customers instantly by gender, age and other demographics in order to show them immediately the goods they are most likely to buy. Given the explosion of consumer goods as well as the rapid increase in the number of vendors on the Internet, the main problem facing customers is how to find the object that they seek, when it is buried beneath a mountain of irrelevant information. A recommender system that can instantly personalize ads in order to solve the customer's search problem is doing both the customer, and the vendor, a favour. New goods are constantly introduced, and new fads sweep the nation, altering shoppers' behaviour. Only a recommender system that is constantly able to learn new patterns can serve consumer needs.

Electronic medical records system user satisfaction and its implications for individual work performance: The case of a university teaching hospital in Rwanda

“…LITERATURE REVIEW Currently, information systems constitute an essential component of organizational management for improving e ciency and performance. However, many systems fail to meet the expectations of their users, which negatively impacts individual and organizational performance [3] [4] [5]. Therefore, the need for research on the successful operationalization of information systems emerged from these challenges and still continues to be an important subject of inquiry [6].…”

“…The purpose of MIS in healthcare settings is to improve e ciency and service delivery to patients to achieve positive outcomes, including saving lives [2]. However, achieving that purpose is not straightforward because many systems fail [3] [4] [5]. The successful implementation of information systems (ISs) requires recurrent and rigorous evaluations that inform the decision making and actions of organizations [6].…”

Electronic Medical Record System user satisfaction and its implication on individual work performance: A case of a University Teaching Hospital in Rwanda

“…The purpose of MIS in healthcare settings is to improve e ciency and service delivery to the patients in order to achieve positive outcomes, including saving lives [2]. However, achieving that purpose is not straight forward because many systems fails [3] [4] [5]. Successful implementation of information systems (IS) requires recurrent and rigorous evaluations that inform decision making and actions of organizations [6].…”

News categorization based on titles with SVM, Naïve Bayesian, random forest, and RNN algorithms

“…[3] It turns out that the time used for searching unclassified documents is twice or even triple the time of searching classified documents. Besides, news aggregators based on text classification could also be applied to sentiment classification, spam detection, Recommender System, and so on [4][5][6].…”