Presented By :Ayesha Khan

Content Introduction Everyday Examples of Collaborative Filtering Traditional Collaborative Filtering Socially Collaborative Filtering Generating Relevant Content Types of Collaborative Filtering References

Introduction 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.[1] Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting likeness information from many users (collaborating).

Why ? Users want an engaging web experience that is both relevant and interesting for them. Given the wide variety of content available on any one website. The situation demands a recommendation system that takes into account both the needs of the individual user and the combined effect of other people who have similar interests.

Everyday Examples of Collaborative Filtering Bestseller lists Top 40 music lists The “recent returns” shelf at the library Unmarked but well-used paths thru the woods Many weblogs Common insight: personal tastes are correlated: If Ayesha and Sadaf both like X and Ayesha likes Y then Sadaf is more likely to like Y especially (perhaps) if Sadaf knows Ayesha[2]

Types of Collaborative Filtering Memory-based Model Based Hybrid

Memory-based CF Memory-based CF algorithms use the entire or a sample of the user-item database to generate a prediction. Every user is part of a group of people with similar interests.a prediction of preferences on new items for him or her can be produced.

Model Based CF The design and development of models (such as machine learning, data mining algorithms) can allow the system to learn to recognize complex patterns based on the training data, and then make intelligent predictions for the collaborative filtering tasks for test data or real-world data, based on the learned models.

Hybrid CF Hybrid CF systems combine CF with other recommendation techniques (typically with content- based systems) to make predictions or recommendations. Content-based recommender systems make recommendations by analyzing the content of textual information, such as documents, URLs, news messages, web logs, item descriptions, and profiles about users’ tastes, preferences, and needs, and finding regularities in the content [4]

Traditional Collaborative Filtering Collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, and the like. [3] The standard approach to making recommendations to a user in order to encourage them to buy a product is through a form of collaborative filtering in which the system tracks all the items a user touches. The resulting database of 1-to-1 relationships between a user and any piece of content is easy to update and quick to access. The system may also keep track of the relationship for items a user has viewed as well as bought.

Traditional Collaborative Filtering

Socially Collaborative Filtering In order to produce a set of recommendations more targeted to the individual, it is necessary to have a richer understanding of how the user interacts with the content. A user can take a range of actions on any piece of content, from strongly positive actions such as creating the content or giving it a very positive rating, to negative actions where a user provides a negative comment about the content. These actions are called socially relevant gestures (SRGs) because they provide insight into how a user perceives the content. [3]

Socially Collaborative Filtering

Generating Relevant Content

References 1. – Wikipedia [Socially Collaborative Filtering: Give Users Relevant Content ] 4.