1. Content-based Recommendation Systems
Group: Tippy

2. Group Members Nerin George Deepan Murugan Thach Tran
Goal Models + Presentation
Deepan Murugan
Domain Models + Presentation
Thach Tran
Strategies + Presentation

3. Outline Introduction Item Representation User Profiles
Manual Recommendation Methods
Learning A User Model
Classification Learning Algorithms
Decision Trees and Rule Induction
Nearest Neighbour Methods
Conclusions
Q & A

4. Introduction
The WWW is growing exponentially. Many websites become enormous in term of size and complexity
Users need help in finding items that are in accordance with their interests
Recommendation
Content-based recommendation: recommend an item to a user based upon a description of the item and a profile of the user’s interests

5. Introduction
Pazzani, M. J., & Billsus, D. (2007). Content- Based Recommendation Systems. Lecture Notes in Computer Science. (4321),

6. Related Research Recommender systems
present items (e.g., movies, books, music, images, web pages, news, etc.) that are likely of interest to the user
compare the user’s profile to some reference characteristics to predict whether the user would be interested in an unseen item
Reference characteristics
Information about the unseen item  content-based approach
User’s social environment  collaborative filtering approach

7. Item Representation Items stored in a database table Structured data
Small number of attributes
Each item is described by the same set of attributes
Known set of values that the attributes may have
Straightforward to work with
User’s profile contains positive rating for 1001, 1002, 1003
Would the user be interested in say Oscars (French cuisine, table service)? ID
Name
Cuisine
Service
Cost
1001
Mike’s Pizza
Italian
Counter
Low
1002
Chris’s Café
French
Table
Medium
1003
Jacques Bistro
High

8. Item Representation
Information about item could also be free text; e.g., text description or review of the restaurant, or news articles
Unstructured data
No attribute names with well-defined values
Natural language complexity
Same word with different meanings
Different words with same meaning
Need to impose structure on free text before it can be used in recommendation algorithm

9. TF*IDF Weighting
First, stemming is applied to get the root forms of words
“compute”, “computation”, “computer”, “computes”, etc., are represented by one term
Compute a weight for each term that represents the importance or relevance of that term

10. TF*IDF Weighting Term frequency tft,d of a term t in a document d
Inverse document frequency idft of a term t
TF*IDF weighting
nt,d is term count of t in d
N is number of documents in the collection
dft is number of documents that contains term t

11. TF*IDF Weighting
The term with highest weight occur more often in that document than in other documents  more central to the topic of the document
Limitations
This method does not capture the context in which a word is used
“This restaurant does not serve vegetarian dishes”
The term “vegetarian” might still have significant weight according to the method and the restaurant might get classified into a group of restaurants which serve vegetarian food.

12. User Profiles
A profile of the user’s interests is used by most recommendation systems
This profile consists of two main types of information
A model of the user’s preferences. E.g., a function that for any item predicts the likelihood that the user is interested in that item
User’s interaction history. E.g., items viewed by a user, items purchased by a user, search queries, etc.

13. User Profiles
User’s history will be used as training data for a machine learning algorithm that creates a user model
“Manual” recommending approaches
User customisation
Provide “check box” interface that let the users construct their own profiles of interests
A simple database matching process is used to find items that meet the specified criteria and recommend these to users.

14. User Profiles Limitations Require efforts from users
Cannot cope with changes in user’s interests
Do not provide a way to determine order among recommending items

15. User Profiles “Manual” recommending approaches
Rule-based Recommendation
The system has rules to recommend other products based on user history
Rule to recommend sequel to a book or movie to customers who purchased the previous item in the series
Can capture common reasons for making recommendations

16. Learning a User Model
Creating a model of the user’s preference from the user history is a form of classification learning
The training data (i.e., user’s history) could be captured through explicit feedback (e.g., user rates items) or implicit observing of user’s interactions (e.g., user bought an item and later returned it is a sign of user doesn’t like the item)
Implicit method can collect large amount of data but could contains noise while data collected through explicit method is perfect but the amount collected could be limited

17. Learning a User Model
Next, a number of classification learning algorithms are reviewed
The main goal of these classification learning algorithms is to learn a function that model the user’s interests
Applying the function on a new item can give the probability that a user will like this item or a numeric value indicating the degree of interest in this item

18. Decision Trees and Rule Induction
Given the history of user’s interests as training data, build a decision tree which represents the user’s profile of interest
Will the user like an inexpensive Mexican restaurant?
Cuisine
Service
Cost
Rating
Italian
Counter
Low
Negative
French
Table
Med
Positive …

19. Decision Trees and Rule Induction
Well-suited for structured data
In unstructured data, the number of attributes becomes too enormous and consequently, the tree becomes too large to provide sufficient performance
RIPPER: a rule induction algorithm based on the same principles but provide better performance in classifying text
RIPPER is a rule induction algorithm closely related to decision trees that operates in a similar fashion to the recursive data partitioning approach described above. Despite the problematic inductive bias, however, RIPPER performs competitively with other state-of-the-art text classification algorithms. In part, the performance can be attributed to a sophisticated post-pruning algorithm that optimizes the fit of the induced rule set with respect to the training data as a whole. Furthermore, RIPPER supports multi-valued attributes, which leads to a natural representation for text classification tasks, i.e., the individual words of a text document can be represented as multiple feature values for a single feature. While this is essentially a representational convenience if rules are to be learned from unstructured text documents, the approach can lead to more powerful classifiers for semi-structured text documents. For example, the text contained in separate fields of an message, such as sender, subject, and body text, can be represented as separate multi-valued features, which allows the algorithm to take advantage of the document’s structure in a natural fashion.

20. Nearest Neighbour Methods
Simply store all the training data in memory
To classify a new item, compare it to all stored items using a similarity function and determine the “nearest neighbour” or the k nearest neighbours.
The class or numeric score of the previously unseen item can then be derived from the class of the nearest neighbour.

21. Nearest Neighbour Methods
unseen item needed to be classified
positive rated items
negative rated items
k = 3: negative
k = 5: positive

22. Nearest Neighbour Methods
The similarity function depends on the type of data
Structured data: Euclidean distance metric
Unstructured data (i.e., free text): cosine similarity function

23. Euclidean Distance Metric
Distance between A and B
Attributes which are not measured quantitatively need to be labeled by numbers representing their categories
Cuisine attribute: 1=Frech, 2=Italian, 3=Mexican.
Item
Attr. X
Attr. Y
Attr. Z A XA YA ZA B XB YB ZB

24. Cosine Similarity Function
Vector space model
An item or a document d is represented as a vector
wt,d is the tf*idf weight of a term t in a document d
The similarity between two items can then be computed by the cosine of the angle between two vectors

25. Nearest Neighbour Methods
Despite the simplicity of the algorithm, its performance has been shown to be competitive with more complex algorithms

26. Other Classification Learning Algorithms
Relevance Feedback and Rocchio’s Algorithm
Linear Classifiers
Probabilistic Methods and Naïve Bayes
These are more complex methods which have been described in the paper but we don’t have time to cover them in this presentation

27. Conclusions
Can only be effective in limited circumstances. It is not straightforward to recognise the subtleties in content
Depend entirely on previous selected items and therefore cannot make predictions about future interests of users
These shortcomings can be addressed by collaborative filtering (CF) techniques
CF is the dominant technique nowadays thanks to the popularity of Web 2.0/Social Web concept
Many recommendation system utilise a hybrid of content-based and collaborative filtering approaches

28. Summary Content-based Recommendation Item Representation User Profiles
Manual Recommendation Methods
Learning A User Model
Decision Trees and Rule Induction
Nearest Neighbour Methods

29. Q & A