Reading notes for week 11

Intelligent Information Retrieval:




In the user profiles for personalized information access, we can see that we can collecting information about the uses, and represent and build the user information profiles. Explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs, rely on personal information input by the users, typically via HTML forms. More sophisticated personalization projects based on explicit feedback have focused on navigation. However, Implicit user information collection only track browsing activity, proxy servers seem to be a good compromise between easily capturing information and yet not placing a large burden on the us.


In order to construct an individual user’s profile, information may be collected explicitly, through direct user intervention, or implicitly, through agents that monitor user activity.


Collecting Information About Users


Methods for User Identification: collecting their information and sharing this with a server via some protocol, logins, cookies and session ids


Methods for User Information Collection


Explicit User Information Collection/ Implicit User Information Collection


Comparing Implicit and Explicit User Information Collection.










Personalized Web Exploration with Task Models:


TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a “task model”, for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models:


1). Retrieve documents along with their relevance scores by submitting the user query to a search engine.


2). Calculate similarity scores between retrieved documents and the model.


3). Calculate combined score of each document by equation that alpha * Task_Model_Score + (1 – alpha) * Search_Score


4). Re-rank the initial list by the combined score from step 3.


The idea of re-ranking is to promote documents, which are more relevant to the user task as measured by their similarity to the task model.