|M.Sc Student||Aharony Na'ama|
|Subject||On Ranking Techniques for Desktop Search|
|Department||Department of Industrial Engineering and Management||Supervisors||Professor Sara Cohen|
|Professor Carmel Domshlak|
|Full Thesis text|
Users tend to store huge amounts of files, of various formats, on their personal computers. As a result, finding a specific, desired file within the file system is a challenging task. This thesis addresses the desktop search problem by considering various techniques for ranking results of a search query over the file system. First, basic ranking techniques, which are based on various file features (e.g., file name, access date, file size, etc.) are considered and their effectiveness is empirically analyzed. Next, two learning-based ranking schemes are presented, and are shown to be significantly more effective than the basic ranking methods. Then, a novel ranking technique, based on query selectiveness, is considered for use during the cold-start period of the system. This method is also shown to be empirically effective, even though it does not involve any learning. Finally, we study ranking schemes in which the ranking is performed in two phases. We compare the effectiveness of such schemes with our one-phase learning schemes.