Presented By :Ayesha Khan

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29 Οκτ 2013 (πριν από 3 χρόνια και 5 μήνες)

137 εμφανίσεις

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.
http://en.wikipedia.org/wiki/Collaborative_filtering



Wikipedia

2.
www.cs.cmu.edu/~wcohen/collab
-
filtering
-
tutorial.ppt


3.
http://www.cisco.com/web/solutions/cmsg/C11
-
484492
-
00_Filtering_wp.pdf

[Socially
Collaborative Filtering: Give Users Relevant Content
]

4.
http://www.hindawi.com/journals/aai/2009/421425/