IR Presentation

Collaborative Filtering

Presented by-Diksha R. Gupta

Roll no.:- 7

Information Filtering

ContentsInformation Filtering– Content-based filtering and

Collaborative filtering Content-based Filtering A content-based filtering model based on

multiple criteria evaluation Collaborative Filtering

Information Filtering 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).

Information Filtering(Cont…)

Information Filtering(cont…)

Information Filtering: main categories

Recommender SystemsSystems for recommending items (e.g. books,

movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

Many on-line stores provide recommendations (e.g. Amazon, CDNow).

Recommenders have been shown to substantially increase sales at on-line stores.

There are two basic approaches to recommending:◦Collaborative Filtering (a.k.a. social filtering)◦Content-based

Content-based Filtering

Collaborative Filtering (Social Filtering)

Collaborative Filtering

Collaborative Filtering(cont..)

Collaborative Filtering

Collaborative Filtering(Cont..)

Methods for collaborative recommendations can be

grouped into two general classes:– Memory-based (or heuristic-based)– Model-based.

Collaborative Filtering(Cont..)

Collaborative Filtering(Cont..)Model-based methods use the collection of

ratings to learn a model, which is then used to make rating predictions.

probabilistic models Markov decision processes based on machine learning techniques

Hybrid Methods

1. implementing collaborative and content-based methods separately and combining their predictions

2. incorporating some content-based characteristics into a collaborative approach

3. incorporating some collaborative characteristics into a content-based approach

4. constructing a general unifying model that incorporates both content-based and collaborative characteristics.

Rocchio’ illustrated

: centroid of relevant documents

Rocchio’ illustrated

does not separate relevant / nonrelevant.

Rocchio’ illustrated

centroid of nonrelevant documents.

Rocchio’ illustrated

- difference vector

Rocchio’ illustrated

Add difference vector to …

Rocchio’ illustrated

… to get

Rocchio’ illustrated

separates relevant / nonrelevant perfectly.

Rocchio’ illustrated

separates relevant / nonrelevant perfectly.

Rocchio Formula

ectorfeedback v negativeectorfeedback v positive

vectorprofile original vectorprofile

0 4 0 8 0 0

1 2 4 0 0 1

2 0 1 1 0 4

-1 6 3 7 0 -3

0 4 0 8 0 0

2 4 8 0 0 2

8 0 4 4 0 16

Original profile

Positive Feedback

Negative feedback

0.1

5.0

25.0

(+)

(-)New profile

THANK ‘S