A Seminar on
WEB PERSONALIZATION
Rishi S. Bhutada (132050017)
What is Web Personalization ?
•Personalization involves using technology to
accommodate the differences between individual...
What is Web Personalization ?
• “Personalization doesn’t just have to be product
recommendations, it can also include inse...
Let us take a real life example. Imagine a scenario:-
•You are going for outing on a holiday to a nearby lake for
fishing ...
Why Web Personalization ?
• Amount of data available on Internet is tremendous.
•So filtering this amount of data to provi...
Why Web Personalization ?
• The most important perspective of showing specific
user related information is to gain the Adv...
Why Web Personalization ?
Let me explain you with help of an example.
Imagine a Scenario.
A person searching a keyword “ap...
Categories of Personalization
•There are three categories of personalization:
1. Profile / Group based
2. Behavior based
3...
Methods of Personalization
There are three broad methods of personalization:
1.Implicit
2.Explicit
3.Hybrid
Implicit Method
•It deals with monitoring user’s personal history of searches
and some user specific details like gender, ...
Implicit Method
Implicit Method
Explicit Method
•With explicit personalization, the web page (or
information system) is changed by the user using the
feat...
Explicit Method
Explicit Method
Hybrid Method
•Hybrid approach combines the two methods:
1.Implicit
2. Explicit
•It combines benefits of both the approach...
Hybrid Method
Personalization Techniques
• Personalization aims to provide users with what
they want or need without requiring them to a...
Content Based Filtering
• Content-based filtering assumes the existence of
content descriptions for each item and builds a
...
Social or Collaborative Filtering
• Social or collaborative filtering is traditionally a
memory-based approach to recommend...
Problem with Collaborative Filtering
• While collaborative filtering is commercially
the most successful approach to
recomm...
Difference Between Social Filtering
and Content Based Filtering
• In contrast to content-based
approaches, social filtering...
Combine Approaches
• A new approach to combine social and
content- based filtering approaches:-
• The development of a new ...
How it goes!!!
• Most collaborative filtering systems represent
user feedback as an n- dimensional vector of
ratings of eac...
How it goes!!!
Problems with this approach..!
• The profiles can thus be thought of as being
cumulative in nature, lacking the knowledge t...
Tackling the difficulties..!
• In practice, collaborative filtering systems have
difficulty in obtaining a large quantity of...
Result if we use Personalization
Result if we don’t use Personalization
Conclusion
• The motivation behind this development was
twofold:-
• First, most businesses today have access to a
knowledg...
Thank You..!
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Web personalization

  1. 1. A Seminar on WEB PERSONALIZATION Rishi S. Bhutada (132050017)
  2. 2. What is Web Personalization ? •Personalization involves using technology to accommodate the differences between individuals...
  3. 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. 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. 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. 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. 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. 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. 9. Methods of Personalization There are three broad methods of personalization: 1.Implicit 2.Explicit 3.Hybrid
  10. 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. 11. Implicit Method
  12. 12. Implicit Method
  13. 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. 14. Explicit Method
  15. 15. Explicit Method
  16. 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. 17. Hybrid Method
  18. 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. 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. 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. 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. 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. 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. 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. 25. How it goes!!!
  26. 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. 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. 28. Result if we use Personalization
  29. 29. Result if we don’t use Personalization
  30. 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. 31. Thank You..!

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