Collaborative filtering with probability and collaborative filtering with privacy Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_projects/9880vr023

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • "Collaborative filtering has seen considerable success in the areas regarding information overload and e-commerce, while the current developed systems are flawed in several respects. Two approaches, the distribution-based algorithm and the blurring profile solution, are proposed to address several outstanding issues. The main findings of this research include: • The proposed distribution-based algorithm, like the traditional nearestneighbor method, is easy to understand and easy to implement, while it can provide a confidence level for each predicted rating. • Compare to the nearest-neighbor method using the Pearson correlation coefficient as a similarity metric, the distribution-based algorithm performs significantly better, especially for the sparse dataset, where less information about the active user and the neighbors are available. • A technology-based solution, blurring each user’s profile by inserting new ratings that did not actually exist, is proposed to protect users’ privacy. To quantify privacy provided by different approaches, the degrees of obscurity and evenly-adding are introduced, where the former is to protect the users’ main interests while the latter is to protect the users’ strange interests. • The proposed approaches can protect the users’ privacy, at the same time, they can maintain the satisfied accuracy, and in some cases, they can even increase the prediction accuracy. This mechanism may be used to fill the missing data, which is required for the most model-based algorithms..."--Conclusions
Resource Type
Date Available
Date Issued
Subject
Rights Statement
Publisher
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2012-08-07T20:34:01Z (GMT). No. of bitstreams: 1 2003-1.pdf: 347341 bytes, checksum: 6ef1f4738983d3b64b5ea96cb4912eda (MD5) Previous issue date: 2003
  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2012-08-07T20:32:14Z No. of bitstreams: 1 2003-1.pdf: 347341 bytes, checksum: 6ef1f4738983d3b64b5ea96cb4912eda (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-08-07T20:34:01Z (GMT) No. of bitstreams: 1 2003-1.pdf: 347341 bytes, checksum: 6ef1f4738983d3b64b5ea96cb4912eda (MD5)

Relationships

In Administrative Set:
Last modified: 07/05/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items