Using social network in recommender systems

Researchers started investigating Social Networks mainly after the work of Stanley Milgram, a Harvard social psychologist. In 1967, Milgram conducted several experiments called the “small world experiment” examining the average path length for social networks of people in the United States. Milgram concluded that any two people in the USA are linked in a social network by a mere “six degrees of separation,” meaning that two randomly chosen people are connected by a short chain of intermediate acquaintances regardless of their geographical and cultural distance. This phenomenon has been confirmed in sociological researches.
The emergence of social networks (e.g., Myspace, Facebook) offered new opportunities for RS. Social networks and virtual communities are the main lineament of Web 2.0 which might make a huge contribution to RSs. Recent studies detected this potential and started investigating the impact of social relations in RS. To date, most of this research has focused mainly on trust, while behavioral theory suggests that other social relations impact people’s advice-taking (e.g., communication frequency, reputation). As far as we know, these social relations have been neglected until now.

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.

Recommender system

Recommender system (RS) emerged to provide solutions for the information overload; today many web sites are being created and changed every day and the amount of information has been increased dramatically creating vast amounts of content in the internet network. RS aims to provide users with the items (movies, music, books, news, web pages) best match their individual preference.

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.