1. Collaborative Filtering Presented by; Ghulam Mujtaba MS CS, IBA, Karachi

2. Collaborative filtering Collaborative filtering (CF) is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. [en.wikipedia.org/wiki/Collaborative_filtering] Collaborative filtering is a method for processing data which relies on using data from numerous sources to develop profiles of people who are related by similar tastes and spending habits. It is achieved by Recommender systems.

3. Recommender system Recommender systems or recommendation engines form or work from a specific type of information filtering system technique that attempts to present information items (films, television, video on demand, music, books, news, images, web pages, etc) that are likely to be of interest to the user. en.wikipedia.org/wiki/Recommender_system

4. CF by Recommender systems.. [2] Recommender systems are often implemented using an automated collaborative filtering (ACF, or CF) algorithm. These algorithms produce recommendations based on the intuition that similar users have similar tastes. That is, people who you share common likes and dislikes with are likely to be a good source for recommendations. Numerous CF algorithms have been developed over the past fifteen years, each of which approach the problem from a different angle, including similarity between users[19], similarity between items[22], personality diagnosis[18], Bayesian networks[2], and singular value decomposition[24]. These algorithms have distinguishing qualities with respect to evaluation metrics such as recommendation accuracy, speed, and level of personalization.

5. CF techniques Memory-based CF Model-based CF Hybrid recommenders

6. CF techniques [1]

7. Refernces [1] Review Article on “A Survey of Collaborative Filtering Techniques” By: Xiaoyuan Su and Taghi M. Khoshgoftaar, Department of Computer Science and Engineering, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA [2] ClustKNN: A Highly Scalable Hybrid Model& MemoryBased CF Algorithm Al Mamunur Rashid, Shyong K. Lam, George Karypis, and John Riedl Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 {arashid, lam, karypis, riedl}@cs.umn.edu