Content-based Recommendation Systems Group: Tippy

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

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

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

Introduction Pazzani, M. J., & Billsus, D. (2007). Content- Based Recommendation Systems. Lecture Notes in Computer Science. (4321), 325-341.

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

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

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

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

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

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.

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.

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.

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

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

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

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

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 …

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 email 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.

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.

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

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

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

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

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

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

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

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

Q & A