Unit 10 Muddiest point and Reading notes.

Muddiest point:

I am fine with this class.

Reading Notes:

User Profiles for Personalized Information Access 

    

 The amount of information available online is increasing

exponentially. While this information is a valuable resource, its sheer volume

limits its value. Many research projects and companies are exploring the use of

personalized applications that manage this deluge by tailoring the information

presented to individual users. These applications all need to gather, and exploit,

some information about individuals in order to be effective. This area is broadly

called user profiling. This chapter surveys some of the most popular techniques

for collecting information about users, representing, and building user profiles.

In particular, explicit information techniques are contrasted with implicitly

collected user information using browser caches, proxy servers, browser agents,

desktop agents, and search logs. We discuss in detail user profiles represented

as weighted keywords, semantic networks, and weighted concepts. We review

how each of these profiles is constructed and give examples of projects that

employ each of these techniques. Finally, a brief discussion of the importance

of privacy protection in profiling is presented.

Content-Based Recommendation Systems

    Recommender systems have the effect of guiding users in personalized way to interesting objects in a large space of possible options. Content-based recommendation system try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in while preferences and interests are stored, with the attributes of contend object, in order to recommend to the user new interesting items.

Personalized web exploration with task models

Personalization Web search has emerge as one of the hottest topics for both the Web industry and academic researchers However, the majority of studies on personalized search focused a rather simple type of search, which leaves an important research topic-the personalization in exploratory searches-as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve.