Everybody rely on recommendations in everyday life from other people either orally or by reviews printed in newspapers or websites. Recommender systems are a subfamily of information filtering systems that explore to predict the 'rating' or 'preference' that user would give to an item. These systems are best known for their use in e-commerce websites where they use input about a customer's interest to generate a list of recommended items. Many recommender systems explicitly rate to represent customer's interest by using only the items that the customers purchase, but can also use other attributes, including items viewed, subject interests and demographic data. They direct users towards those items that meet their needs by reducing unwanted information spaces. To perform recommendation a number of techniques have been proposed, including content-based, collaborative, and hybrid techniques. To improve performance and to outweigh the drawbacks of individual recommendation techniques, these techniques are sometimes combined to form hybrid recommenders. This paper is categorized into seven sections. Section-I presents the introduction related to the recommendation systems used in the social networks and online Web systems, section-II critically analyzed the related literature work about collaborative recommendation, contentbased recommendation, and hybrid recommendation, section-III describes the business aspects of recommender systems, section-IV describes various ways of displaying recommendations to a customer, section-V investigates the various recommendations techniques and their limitations and section-VI provides the conclusion of the recommender systems. In this paper the efforts are made to review and discover the techniques to investigate the proper usage of recommender systems in the e-commerce applications.

The vision for Web 3.0 (also known as Semantic Web) is the ability to create meaning out of huge quantity of qualitative data. Existing data can be interconnected for further uses. Web 2.0 focused on the users interaction with others whereas Web 3.0 focus more on the users themselves. The advantages of Semantic Web and E-commerce give rise to social commerce (also referred as f-commerce). The future of business lies on the "social" factor and it is this factor which gives rise to a new kind of connected consumers who are becoming influential in their own right. This paper explores a very specific instance of Semantic Web -Social Recommender System. This paper discusses the likelihood of converting social data into quantitative information and using this information to power social recommendations. This paper first outlines the benefits of social commerce over ecommerce platform. Then the related literature work regarding hybrid recommenders is discussed. Next it is discussed how to predict ratings from a user-item rating network and friend's network and then how to unify similarity matrices obtained from different networks. And lastly this paper covers the social hybrid product recommender algorithm and its experimental evaluations to predict its efficiency.

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