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1) Proactive- query less recommendation engine (based on Automatic Collaborative Filtering mechanism )
2) Reactive – query based content personalization
Both based on user personalization where user data is gathered from web server logs
Data used to learn about the implicit and explicit preferences of individual users. This information is used to personalize their information retrieval processes
Each user profile records relevancy information to discriminate between those jobs that the user looks at or considers, and those that she is truly interested in
Graded profiles on a user make it possible to 1) recommend jobs matching the interest based on what similar users have previously liked 2) supplement each user’s search queries with additional relevant search terms, and filter the retrieval results to weed out irrelevant hits
Reliance on direct user relationships, for example between A and B
Recommendations may be based on a small number of profiles with low degrees of similarity
May even result in no recommendations
Ignores potential indirect relationships between users. C may have the same job taste as A, but as C has seen a different set of jobs, this will not be recognized.
Indirect transitive relationship ; user B is directly related to users A and C
Group users prior to recommendation – profiles are clustered into virtual communities such that all of the users in a given community are related
The single-link clustering technique can be used with a thresholded version of the similarity metric
Each community is a maximal set of users such that every user has a similarity value > than the threshold with at least one other community member
Each target user is recommended the most frequently occurring jobs in its virtual community
2. Case-Based User Profiling for Content Personalization Two-step personalized retrieval engine When a user enters a new search query, a server-side similarity-based search engine is used to select a set of similar job cases . This is followed by Personalization, a post-processing retrieval task where the result-set is compared to a user profile in order to filter-out irrelevant jobs .
Automated Collaborative Filtering Applications for Online Recruitment Services Rachael Rafter, Keith Bradley, Barry Smyth ;Smart Media Institute, Department of Computer Science, University College Dublin, Ireland
Case-Based User Profiling for Content Personalisation Keith Bradley , Rachael Rafter & Barry Smyth Smart Media Institute Department of Computer Science, University College Dublin, Ireland
Navigating Nets : Simple algorithms for proximity search Robert Krauthgamer & James R Lee
User Profiles for Personalized Information Access Susan Gauch Mirco Speretta Aravind Chandramouli and Alessandro Micarelli ; Electrical Engineering and Computer Science Information & Telecommunication Technology Center , Lawrence Kansas