1. Collaborative Filtering Zaffar Ahmed

2. Overview
Method of making automatic predictions (filtering) about the interests of a user or item by collecting state information from many users or items (collaborating).
It analyzes data which relies on using data from numerous sources to develop profiles of people who are related with similar tastes and spending habits.
It is based on ‘word-of-mouth’ idea
Gives reliable recommendations

3. Facts
It needs a lot of stored data for reliable recommendations for the active user.
Bigger population – more useful and effective recommendtions will be produced (Smart Mobs)
Small data – shows false connections or poor predictions of active user tastes
Suffers from cold start problem – database needs to be populated first.

4. Methodology Divided into two steps
Look for users who share the same rating patterns with the active user
Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user.

5. Types of Collaborative Filtering
Memory-based: uses user rating data to compute similarity between users or items
Neighborhood-based CF
calculates similarity b/w two users or items, produces a prediction for the active user taking the weighted average of all the ratings.
Item/user based top-N recommendations
identifies the K most similar users using similarity based vector model.
Locality sensing hashing: It implements nearest neighbor mechanism in linear time.
Advantages:
explainability of the results, 2) easy to create and use, 3) new data can be added easily and incrementally
Disadvantages:
1) depends on human rating, 2) performance decreases when data gets sparse, 3) it can not handle new users or items

6. Types of Collaborative Filtering
Model-based: models (ontologies) are developed using data mining, machine learning algorithms to find patterns based on training data. It has more holistic goal to uncover latent factors that explain observed ratings.
Bayesian Networks
Clustering models
Latent semantic models
Singular value decomposition
Probabilistic latent semantic analysis
Multiple multiplicative factor
Latent dirichlet allocation
Markov decision process based models
Advantages
Handles sparsity better than memory based algos: improves scalability and prediction performance.
Disadvantages
Expensive model building

7. Types of Collaborative Filtering
Hybrid
Combines model-based and memory-based CF algos.
overcomes the limitations of native CF approaches.
Advantages
Improves prediction performance
Disadvantages
Increased complexity
Expensive to implement

8. Thank you