Reading Note For Week 11

Content-based recommendation systems: systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems analyze item descriptions to identify items that are of particular interest to the user.

Item Representation
(1) Items that can be recommended to the user are often stored in a database table
(2) Unstructured data: free text data
(3) Many domains are best represented by semi-structured data in which there are some attributes with a set of restricted values and some free-text fields.

Two types of information on user profile
(1) A model of the user’s preferences
(2) A history of the user’s interactions with the recommendation system

Creating a model of the user’s preferewnce from the user history is a form of classifica- tion learning. The training data of a classification learner is divided into categories, e.g., the binary categories “items the user likes” and “items the user doesn’t like.”
(1) Decision Trees and Rule Induction
(2) Nearest Neighbor Methods
(3) Relevance Feedback and Rocchio’s Algorithm
(4) Linear Classifiers
(5) Probabilistic Methods and Naïve Bayes


In the modern Web, as the amount of information available causes information over- loading, the demand for personalized approaches for information access increases. Personalized systems address the overload problem by building, managing, and represent- ing information customized for individual users.

Collecting information about users
(1) The information col- lected may be explicitly input by the user or implicitly gathered by a software agent.
(2) Depending on how the information is collected, different data about the users may be extracted.

User Profile Construction
(1) Building Keyword Profiles. Keyword-based profiles are initially created by extracting keywords from Web pages collected from some information source, e.g., the user’s browsing history or book- marks.
(2) Building Semantic Network Profiles: semantic network-based profiles are typically built by collecting explicit positive and/or negative feedback from users.
(3) Building Concept Profiles: