Yesterday, i was reading a book on Recommender Systems, since i being a novice to this field of study, i thought lets post an excerpt on it on my blog for others with similar interest.
An extract from the book, “Recommender Systems Handbook by F Ricci et al, (Springer, 2011))
To provide a first overview of the different types of Recommendation Systems, we want to quote a taxonomy provided by  that has become a classical way of distinguishing between recommender systems and referring to them.  distinguishes between six different classes of recommendation approaches:
Content-based: The system learns to recommend items that are similar to the ones that the user liked in the past. The similarity of items is calculated based on the features associated with the compared items.
Collaborative filtering: Recommends to the active user the items that other users with similar tastes liked in the past. The similarity in taste of two users is calculated based on the similarity in the rating history of the users. This is the reason why its also referred to as “people-to-people correlation.” Collaborative filtering is considered to be the most popular and widely implemented technique in Recommender Systems. Neighbourhood methods focus on relationships between items or, alternatively, between users. An item-item approach models the preference of a user to an item based on ratings of similar items by the same user. Nearest-neighbours methods enjoy considerable popularity due to their simplicity, efficiency, and their ability to produce accurate and personalized recommendations.
Demographic: This type of system recommends items based on the demographic profile of the user. The assumption is that different recommendations should be generated for different demographic niches. Many Web sites adopt simple and effective personalization solutions based on demographics.
Knowledge-based: Knowledge-based systems recommend items based on specific domain knowledge about how certain item features meet user’s needs and preferences and, ultimately, how the item is useful for the user. Notable knowledge based recommender systems are case-based. In these systems a similarity function estimates how much the user needs (problem description) match the recommendations (solutions of the problem). Here the similarity score can be directly interpreted as the utility of the recommendation for the user.
Community-based: This type of system recommends items based on the preferences of the users friends. This technique follows the epigram “Tell me who your friends are, and I will tell you who you are”. Evidence suggests that people tend to rely more on recommendations from their friends than on recommendations from similar but anonymous individuals. The research in this area is still in its early phase and results about the systems performance are mixed.
Hybrid recommender systems: These RSs are based on the combination of the above mentioned techniques. A hybrid system combining techniques A and B tries to use the advantages of A to fix the disadvantages of B. For instance, CF methods suffer from new-item problems, i.e., they cannot recommend items that have no ratings. This does not limit content-based approaches since the prediction for new items is based on their description (features) that are typically easily available.
 Burke, R.: Hybrid web recommender systems. In: The Adaptive Web, pp. 377–408. Springer Berlin / Heidelberg (2007)