I am begining to write a survey of approches on personalized search. In this post, I present the category of approches to personalized search. It is as follows ....
Categorization of Personalized Search Approaches
First of all, search is not a solved problem. Morever, with the tremendous growth in the available information on the web, personalized search is increasinly becoming an active research area.
Actually there are a variety and a growing literature of approaches proposed for Personalized search. A category of the approaches can be
1) Link Based Approaches using the Graph structure of the web, Primiarily Extending PageRank(what google uses), Hubs and Authorities..
2) Domain Specific Personalized based on Ontologies etc.
3) Content Based approaches (based on Vector Model in Information Retrieval)
4) Machine learning Based Approaches
5) Approaches based on Linear Algebra
6) Recommemdnation based personalized search (using collaborative filteirng and content based filteirng)
7) Based on Long term history Short term history of the user from web log etc etc. etc.
All the existing approaches to personalized saarch in the literature can more or less be categorized in one more of of the approches. Each approah can fall into one or more categories.
For example, a machine learning based approach uses the content of the page.. etc etc.
The visualization of this categorization can be better done in terms of sets. Each of the 7 categories
can be represented as a set. certain sets contain certain other sets. there are small overlaps, big overlaps accordingly. The approaches belonging to each of the above category are the elements of the respective sets.