Information filtering is the process of monitoring large amounts of dynamically generated information and identifying the subset of information likely to be of interest to a user based on their information needs. It represents the user's interests and identifies only pieces of information they would find interesting. There are three main categories of information filtering: collaborative filtering which uses recommendations from other users; content-based filtering which uses a comparison between item content and user profiles; and hybrid filtering which combines aspects of collaborative and content-based filtering. Feedback techniques can also be used to continually update and improve filtering.

1. Information Filtration
Syed Ali Jafar Zaidi
MS(CS) Islamia University Bahawalpur

2.  the systematic process of collecting and cataloging
data so that they can be located and displayed on
request.
 Computers and data processing techniques have
made possible the high-speed, selective retrieval of
large amounts of information for government,
commercial, and academic purposes.
information storage and retrieval

3.  An automated storage and retrieval system consists
of a variety of computer-controlled systems for
automatically placing and retrieving loads from
defined storage locations.
 Information retrieval is about fulfilling immediate
queries from a library of information available.
What is Retrieval System?

4.  Information Filtering is the process of monitoring
large amounts of dynamically generated information
and pushing to a user the subset of information likely
to be of her/his interest (based on her/his information
needs)
What is Information filtering

5.  Information filter is when applied to an information
evaluates that whether the query or desired result is
according to the interest of the user or not?
 it represents the user's interests and as such has the
goal of identifying only those pieces of information
that a user would find interesting.
What is Information filtering

6.  Information filtering is finding material (usually
documents) of an unstructured nature (usually text,
images or videos) that satisfies an information need
from within large collections
 Information Filtering is about processing a stream of
information to match your static set of likes, tastes
and preferences.
Some more about Information
Filtering

7.  A clipper service which reads all the news articles
published today and serves you content that is
relevant to you based on your likes and interests.
Example

8. Information Filtration

9.  Retrieval systems are generally concerned with
satisfying a user’s one-off information need (query)
 filtering systems are usually applied to attaining
information for a user’s long term interests (profiles)
Comparison of IR and IF

10.  Vector Space Model
 Probablistic Network
Feedback Techniques

11.  Vector-Space Model:
 Pk+1 = Pk + Xn1
 k=1 Rk n1 Xn2 k=1 Sk n2
 where PK+1 is the new profile, P k is the old profile, Rk is a
vector representation of a relevant article k, Sk is a vector
representation for non–relevant article k, n1 is the number
of relevant documents and n2 is the number of non–
relevant documents. The values and determine the
relative contributions of positive and negative feedback,
respectively
Vector Space Model

12.  Collaborative Filtering (Social Filtering)
 Content based Filtering
 Hybrid Filtering
Main categories

13.  Collaborative filtering is a method of making
automatic predictions (filtering) about the interests
of a user by collecting preferences or taste
information from many users
 Filters information by using the recommendations of
other people
Collaborative Filtering(Social
Filtering)

14.  Tapestry system
The Tapestry system was developed to aid users
in the management of incoming news articles or mails.
It was developed specifically for people working in
work-groups. Users can use content filters to select
articles and can also retrieve/filter articles based on the
recommendations of other users. On reading articles,
users are asked to provide a rating of the article.
Collaborative Filtering Systems

15.  GroupLens
GroupLens is a “distributed system for gathering,
disseminating, and using ratings from some users to
predict other user’s interests in articles”
 Recommender Systems
Other common examples of social or collaborative filtering
include recommender systems. In these systems, users
rate different interests, such as, videos
Collaborative Filtering Systems

16. Every Day Example of CF

17. Example

18.  Recommends items based on a comparison between
the content of the items and a user profile.
 It needs a formal model to represent both documents
and user profiles and to compute the similarity
between a document and each user profile.
Content Base Filtering

19.  It is the combination of content based and collaborative
filtering
 implementing collaborative and content-based methods
separately and combining their predictions
 incorporating some content-based characteristics into a
collaborative approach
 incorporating some collaborative characteristics into a
content-based approach
 constructing a general unifying model that incorporates
both content-based and collaborative characteristics.
Hybrid Filtering

20. Why we Need It
 A huge range of content such as news, shopping and social networking sites that can greatly
reduce the productivity.
 Block malware and other content that is or contains hostile, intrusive, or annoying material
including spam, computer viruses, worms, Trojan horses, and spyware.