The online payment services can be divided into two types:

Prepaid cards

It is a pre-funded card, the user only spend what money he load on it and he can typically get it without a credit check or bank account. User can simply purchase the prepaid card form a cards provider, load it with the cash amount you choose and make online shopping in online merchants who support the provider cards.

This type is most acceptable in the Arab region because it safe and secured. Customers utilize it to pay for online games, online services, download websites, and local e-Commerce websites. There are two players who provide such services:

i.            OneCard:  was initially launched in Saudi Arabia during April 2004. It is a prepaid online payment allowing users to access the most comprehensive portfolio of Internet connectivity (ADSL), communication services, mobile services, premium subscriptions and paid content from the Web and much more. OneCard domain has further expansion by 2008 to encompass Egypt, GCC countries L, Iran, and North Africa.

ii.            Cashu: introduced by Maktoob.com, the largest and most popular online Arab community with more than 3.5 million registered users. The biggest markets for cashU are Saudi Arabia, Kuwait, Syria and Egypt.

Postpaid cards

According to this type of cards, at the end of the month the customer receives a bill which shows the itemized billing for the month.This type commonly refers to mobile service providers and credit cards which are not very popular in the Arab countries because of security concerns. Arabs have less familiarity with this kind of service, and banks in Arab countries don’t encourage using such cards.AlaaNumair, the senior research analyst for Arab Advisors Group said “People do not really understand how to use a credit card, or they have some issues about the security behind using credit cards, they basically are afraid to use their credit cards online.”

Mobile phone payments

Statistic show a very high mobile penetration rate in the Arab world, for instance, at the end of September 2009, the rate for mobile phone users in the UAE stood at 193.5 percent, followed by Saudi Arabia with a rate of 165.5 percent and Jordan with 98%. Therefore,this method can be realistic solution for Arab consumer.

In 2009 Dubai launches first Mobile Payments service inthe Middle East and North Africa region (MENA) region. But until writing this, there are no statistical reports about the usage of this type of service in the Arab countries.

As for e-Commerce, experts expect that “E-commerce transactions in the Middle East are expected to grow beyond 15 per cent in 2010, driven by infrastructural development, strong government focus on online services, and rapidly growing internet penetration.”
Reports raked Saudi Arabia the first among Arab countries in terms of e-Commerce growth where 3.5 million internet users or 14.26 percent of the population were engaged in e-Commerce transactions. And 3% of Jordan’s population use e-commerce during 2010.
Arab Advisors Group revealed that 34.6% of adult Internet users in Egypt use e-commerce. The Arab Advisors Group estimates the number of Egypt internet users who utilize e-commerce to be around 2.36m which is around 3% of the total population in Egypt.
A survey conducted by the Arab Advisors Group in 2007 showed that51.2 percent of Internet users in the United Arab Emirates (UAE) reported purchasing products and services online and through their mobile handsets over the past 12 months. Based on the survey findings, the Arab Advisors Group estimates e-commerce users in the UAE to exceed 1.16 million consumers who have spent over US$ 1.15 billion over the past 12 months.

In the Gulf Cooperation Council (GCC) around 43% of internet users have started to make e-commerce purchases at least once a month.
It can be derived from the studies above that considerable amount of Arab users are engaging in e-Commerce and this number is expected to raise in the coming years as the internet penetration growth.

• New user problem: content-based systems have to learn the user profile based on historic transactions; therefore for a new user the system can provide no recommendations.
• Machine readable item: content-based systems are intended mainly for text-based items that a computer can automatically parse. Multimedia items (e.g., audio and video clips) are therefore out of the game, although these items spread widely on the internet. In order to allow such items to be recommended, they must be manually specified by a human editor, who can add some machine-readable content, such as Meta data or tag terms.
• Over fitting problem: the content-based system recommends items similar to the ones the user liked in the past, and this may lead to a situation in which items only from a specific topic are recommended to the user while other relevant items that the user might be interested in but that belong to different topics might be ignored. Intelligent RS should be able to recommend items from diverse topics.

The content-based approach is based on the most representative set of keywords (features) extracted from the items, and therefore such systems cannot distinguish between a well-written and a badly-written document that both use the same terms. This limitation stems from the fact that this approach does not take other users’ opinion about an item into consideration, but only the similarity of the item with other items the user previously found relevant.

In Collaborative Filtering (CF), the main idea is to automate the process of “word-of-mouth.” In the offline world we can seek advice from trusted people whom we know, while in the online world we can get advice from trusted people worldwide and exploit their collective wisdom and experience. This approach requires the user to express the sources they trust (we refer to those users as recommenders, sources or predictors), but this involves significant user effort. The early CF recommender systems, based their recommendations on data elicited by asking people to specify manually their “Shared-Interest” people, without any automatic process for detecting the similar behavior of other users.

Researchers started investigating more automatic approaches, and in the middle of 1995, similarity-based CF was suggested, here we using similarity as a proxy for the source’s trustworthiness. No user effort would be needed, since the process of finding trusted sources would be automatically performed. The first system to achieve this approach being the MovieLens system. Many web-based RS applications are being developed for various domains, and many on-line stores (e.g., Amazon, MediaUnbound, Netflix, MoodLogic, CDNOw, SongExplorer, etc) use these techniques to recommend items that meet their customers’ preferences and hence substantially increase their sales.

Users communicate via E-mail applications (e.g., Gmail: Google mail service) and produce interaction-based social ties. Users can also contact friends and search for new friends (e.g., Facebook) producing friendship-based social ties; others may participate in online auctions in C2C trade environment (e.g., eBay.com) producing reputation-based ties. These are a few examples of the different platforms which enable members to establish different social ties with each other. These social ties are more available today than ever before, and we believe that such information regarding users’ relationships could potentially be exploited for improving the performance of recommender systems.

Today, having realized the usefulness of and the rich information stored in such networks, many applications have been built that exploit these advantages. In the internet network there are more than 200,000,000 user accounts and over 141 different social networks. Eighteen sites exist that have over 1,000,000 members. Popular examples of such networks are MySpace, which has 60,000,000 user accounts and “Friendster,” which has over 27,000,000. Facebook is the most popular social network having more than 90 million users, and more than 13,000 social applications [26]. These networks were established for several purposes: Blogging, Business, Dating, Entertainment, etc.
The social networks that emerged created virtual communities. Community members share information such as photos, personal information, hobbies, professional knowledge, etc. (e.g. Friendster.com). Communities enable their members to conduct social relations similar to those that people conduct in the real world. Users in social networks collaborate to satisfy their own needs.

These facts encourage the use of social network’s characteristics for generating more effective recommendations and present new opportunities for using the collaborative filtering approach. Many web applications enable people to find friends worldwide and to conduct reciprocal social relations with them (e.g., Facebook, Sleeper, MovieCritics and Real.com).

The internet provides an opportunity for people to interact with each other, and thus many types of social relationship are established among users, of which businesses, friendship and colleague relations are examples.
Research studies in the marketing and applied psychology fields have identified four salient social measures that are relevant in the advice-taking context. These are cognitive similarity, tie strength, trust, and social capital. It has also been shown that different types of social relations impact recipients’ advice-taking in different ways. Therefore, social relationships can be incorporated into recommender systems to provide users with more realistic recommendations. The collaborative filtering (CF) approach commonly used in recommendation systems emerged in the mid-1990s, and has since become the de-facto standard. Collaborative filtering tries to mimic the social process of advice-seeking through the users’ cognitive similarity. Although, this method has been proved useful for producing accurate recommendation, it produces inaccurate predictions in many real situations, especially in sparse data sets.
Therefore, enhancing collaborative filtering can be achieved by integrating types of social measures (e.g., tie strength, trust, and friendship). Social measures can be differ, so we need to identify the effectiveness of different types of social relations and we need to be able to identify the most valuable measure for recommender systems (for different context). For evaluation purpose, we can develop a social filtering models that incorporated these social measures and conduct an empirical experiment to test these models. We can explore several social-based prediction methods and to benchmark these methods against the traditional CF method.
Incorporating social relationships into information retrieval systems in general can yield to more accurate results than pure IR systems. But, inferring social relationships from social networks (e.g,: facebook,myspace and others), integrating them into IR systems, and finally evaluating the produced model still need more works and efforts.

OPM treat every system and Information System in practical as a set of objects and process that interact with each other. Process can consume, produce, or change the status of the object. At any point of time, objects are at some state, process transform these states producing different “snapshot” of the system.

A system involved two sides: static-structural side and dynamic-behavioral side. Modeling languages developed over the years can be classified into three categories:

1. Structure (Object Oriented): such modeling languages only specify the static structure of the system. ERD and Class diagrams are examples for such methodologies.
2. Behavior (Process Oriented): other modeling language focus mainly on the behavior side of the system and neglecting the structure side of the system. DFD, Sequence Diagram, Collaboration Diagram could be good example for these type of methodologies.
3. Hybrids: this type of methodologies try to specify all system aspect but with different approaches. UML uses multiple diagrams specifying both the behavior and the structure of the system.

OPM principle is that objects and processes are the two types of equally important things (entities) required to describe a system in a single, unifying model. In OPM the structural aspect of the system specified by structural relations connected objects or process, while the behavior aspect of the system specified by a procedural relation connected process and object.

Today many web based RS Application being developed for various domains, and many on-line stores (e.g. Amazon, MediaUnbound,Netflix, MoodLogic, CDNOw, SongExplorer, etc) recommend items that meet their customers’ preferences and hence substantially increase their sales.

Algorithms for RS can be divided into two types, model based and memory based. Model-based algorithms use the collection of ratings to learn a model, which is then used to predict the ratings, similar to the method used in data mining for discovering patterns from observations. In these algorithms we only store the model, rather than the ratings and for each request, the model is consulted for prediction. Examples for model based algorithms are clustering, Bayesian model, and k-means.

In the other side, Memory based algorithms use heuristics that predict new items based on the entire collection of previously rated items by other similar users. The predicted item is usually computed as an aggregate of the ratings of some other users (k similar users) for the same item.

Content-Based approach is the first approach to be proposed for RS, it comes from the information retrieval field. In this approach the user will be recommended items similar to the ones the user preferred in the past, or to the areas of interests of the user (Balabanovic&Shoham, 1997a;Clypool et al .,1999).

Today Web 2.0 offered new opportunities for RS, social networks (e.g., Myspace, Facebook) and virtual communities are its main lineament which might have a huge contribution for RS. Recent researches detected this potential and start investigating the impact of social relations in RS, to date most of this researches focus mainly on trust, while the behavioral theory suggest other social relations that impact people’s advice taking (e.g., communication frequency, reputation), these social relations were neglected. Users communicate via E-mail application (e.g., Gmail: Google mail service) and produce interaction social tie, users can contact friends and search for new friends (e.g., Facebook) producing friendship social ties; others may participate in online auctions in C2C trade environment (e.g., eBay.com) producing reputation ties. These are a few example of different platform which enable members to establish different social ties with each others, such these social ties are more available today than before, and we believe that such information regarding users’ relationships could potentially be exploited for recommender system.