1. A Seminar on
WEB PERSONALIZATION
Rishi S. Bhutada (132050017)

2. What is Web Personalization ?
•Personalization involves using technology to
accommodate the differences between individuals...

3. What is Web Personalization ?
• “Personalization doesn’t just have to be product
recommendations, it can also include inserting any
content like images or text or customizing content
which is already there !”
•“…in any format”
•“…that is relevant to the individual user, based on
the user’s implicit behavior and preferences, and
explicitly given details”

4. Let us take a real life example. Imagine a scenario:-
•You are going for outing on a holiday to a nearby lake for
fishing with your friends.
•You are carrying foodstuffs you like to eat with you like
sandwiches , cold drinks ,chocolate ice-creams etc.
•But to attract a fish towards you need to feed them with
stuffs that fishes like, i.e. worms not chocolate ice-cream.
•It doesn’t matter how much or what variety of
information you have, to facilitate your users
you have you need to provide them with
information relevant to that specific user.
Why Web Personalization ?

5. Why Web Personalization ?
• Amount of data available on Internet is tremendous.
•So filtering this amount of data to provide good user
experience we need to have a certain type of mechanism.
• Also the words have different meanings, semantics with
respect to geographical locations of users, interest of users
etc.
• The vendors who sell their products online should be able
to attract and reach to only the users having interest in
products their firm offers.

6. Why Web Personalization ?
• The most important perspective of showing specific
user related information is to gain the Advantage.
• Showing proper advertisement to that particular user.
•Social Network websites use personal data to provide
relevant advertisements for their users. Websites
like Google and Facebook are using account information
to give better services.

7. Why Web Personalization ?
Let me explain you with help of an example.
Imagine a Scenario.
A person searching a keyword “apple” on any
Search Engine.
Search Engine should return Apple’s electronic
gadgets if that person has interest in Gadgets or
should return cooking recipes related to apple if
that person has interest in cooking.

8. Categories of Personalization
•There are three categories of personalization:
1. Profile / Group based
2. Behavior based
3. Collaboration based
•Web personalization models include rules-based
filtering, based on "if this, then that" rule
and collaborative filtering which serves relevant
material to customers by combining their own personal
preferences with the preferences of like-minded others.

9. Methods of Personalization
There are three broad methods of personalization:
1.Implicit
2.Explicit
3.Hybrid

10. Implicit Method
•It deals with monitoring user’s personal history of searches
and some user specific details like gender, geographical
location etc. to give him proper output for further queries.

11. Implicit Method

12. Implicit Method

13. Explicit Method
•With explicit personalization, the web page (or
information system) is changed by the user using the
features provided by the system.
•User is provided with some generalized categories from
which user has to choose the most relevant to him
regarding his requirements.

14. Explicit Method

15. Explicit Method

16. Hybrid Method
•Hybrid approach combines the two methods:
1.Implicit
2. Explicit
•It combines benefits of both the approaches.
•The highest amount of efficiency is achieved by this
method only.

17. Hybrid Method

18. Personalization Techniques
• Personalization aims to provide users with what
they want or need without requiring them to ask
for it explicitly .
• This is achieved through either Content-Based
Filtering or Social Filtering.
• In both cases a user model is created from data
gathered explicitly and/or implicitly about user
interests and used to recommend a set of items
deemed to be of interest to the user.

19. Content Based Filtering
• Content-based filtering assumes the existence of
content descriptions for each item and builds a
model of user preferences using these content
descriptions and the rating data of the user.
• The model is then used to predict the likelihood
of items, not currently viewed by the user, being
of interest to the user.
• The most likely items of interest to the user
constitute the recommendation set.

20. Social or Collaborative Filtering
• Social or collaborative filtering is traditionally a
memory-based approach to recommendation
generation.
• The recommendation process consists of
discovering the neighborhood of the active user,
i.e. other users that have a similar rating vector to
that of the active user, and predicting the ratings
of items, not currently viewed by the active user,
based on ratings of these.
• The user, recommendations for whom need to be
generated items by users within the active user’s
neighborhood.

21. Problem with Collaborative Filtering
• While collaborative filtering is commercially
the most successful approach to
recommendation generation, it suffers from a
number of well-known problems including the
cold start/latency problem, sparseness within
the rating matrix, scalability, and robustness .

22. Difference Between Social Filtering
and Content Based Filtering
• In contrast to content-based
approaches, social filtering does not
traditionally use any item content
descriptions.

23. Combine Approaches
• A new approach to combine social and
content- based filtering approaches:-
• The development of a new approach to the
recommendation of items in sparse item-
rating spaces;
• A new metric for measuring similarity
between two user-rating vectors in the
presence of an item ontology;

24. How it goes!!!
• Most collaborative filtering systems represent
user feedback as an n- dimensional vector of
ratings of each item in the item set:-
• Three user profiles (rows) for a simple movie
recommendation system with an item set (the set
of movies stocked by the retailer) consisting of
just eight movies the item set.
• As feedback, in the form of item ratings, is
received from a user, it is simply added to the
user profile, with no knowledge about the
recency or context of the individual ratings being
stored.
Contd…

25. How it goes!!!

26. Problems with this approach..!
• The profiles can thus be thought of as being
cumulative in nature, lacking the knowledge that it
would require to adapt to changes in user context.
• As a result, items rated by a user in one context may
be used to recommend items within a different user
context ,effectively adding noise to the
recommendation process, reducing recommendation
accuracy.
• Similarly, items rated a long time ago may be used to
recommend items at the present time, ignoring
changing user interests.
• There is a need to build more expressive user models
that can adapt to changes in user context and interest
drift.

27. Tackling the difficulties..!
• In practice, collaborative filtering systems have
difficulty in obtaining a large quantity of user
rating data and this often results in poor
performance.
• The difficulty in obtaining explicit ratings for
items has resulted in considerable interest in
methods for obtaining implicit ratings for items.
• On the Web, a rather noisy but data rich source
for implicit item ratings is the user clickstream.
• Studies have shown that the ‘linger time’ of a
user on a page is a good indicator of user interest

28. Result if we use Personalization

29. Result if we don’t use Personalization

30. Conclusion
• The motivation behind this development was
twofold:-
• First, most businesses today have access to a
knowledge base of items offered to their
customers .Intuitively, the use of such domain
knowledge within the recommendation process
should result in more accurate recommendations.
• Second, visitors to a Web site may have different
contexts/motivations for visiting the Web site.

31. Thank You..!