According and they always provide various methods for improvement.

According to Ricci. et al. 2011 recommendation engines, also referred to as recommender systems are applications or programs that uses various algorithms techniques to analyse, gather and forecast different kind of data in order to predict information and generate recommendations for users, more companies have recently started deploying recommendation engines in to their business’s (Ricci, Rokach, & Shapira, 2011). 

Amazon.com, YouTube, Netflix, Yahoo, TripAdvisor, Last.fm, and IMD, uses recommender engines on their internet sites. Additionally, many media companies are now setting up recommender systems as part of the services they offer to their customers. For instance, the company Netflix, an online rental service films and movies, gave one-million-dollar award to a team that improved the performance of the company’s recommender engine  (Koren, Bell , & Volinsky, 2009)

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Recommendation engines are valuable and useful software that handles and analyses massive amounts of data, then delivers a reduced and focused recommendations for users. The engines can be classified into different groups or categories, that each one of them represents and used for a specific purpose. According to (Jenson, 2017) The list of recommendation engines contains the following:

·         (SaaS), known as software as a service recommender system

According to Amazon web services, Software as a service is an application delivery model which allows users to utilize a software solution over the Internet. SaaS revenue engines are mostly payment based, that users must subscribe and pay a fee for the service.

In a recent study that was done by Afify et. al, 2016, proposed a (SaaS) recommender system and based it on a hybrid filtering that personalizes quality of service measurement, software as a service recommender system semantically manages user requests to detect business-oriented matching services, that are later ?ltered to fulfil the quality of services for user requirements and the service characteristics. In their work, the authors apply hybrid filtering techniques to authenticate the services set based on services metadata and the interests of the user. Lastly, the suggested set of services is arranged (M. Afify, F. Moawad, L. Badr, & F. Tolba, 2016).

 

 

Software as a service is a growing model that includes extensive range of business, (SaaS) Recommender systems have many benefits such as: they mostly charge low fees, they are easy to integrate and to use, and they always provide various methods for improvement. However, (SaaS) recommender systems have some challenges in the development process, that involves dealing with multi-tenancy, repository and handling an enormous quantity of information and other softer concerns like keeping a client’s sensitive data safe on remote servers (Jenson, 2017).

(Jenson, 2017) created a list of some the (Saas) recommender systems that includes the following:

a.

 

·         Open sources recommender system engines

Open sources are considered as  a non- (Saas) recommender systems