Skip navigation
Please use this identifier to cite or link to this item:
Title: Sparsity, robustness, and diversification of Recommender Systems
Authors: Zhang, Zhuo
Advisors: Kulkarni, Sanjeev
Contributors: Electrical Engineering Department
Keywords: Collaborative Filtering
Recommender System
Subjects: Electrical engineering
Computer science
Issue Date: 2014
Publisher: Princeton, NJ : Princeton University
Abstract: Recommender systems have played an important role in helping individuals select useful items or places of interest when they face too many choices. Collaborative filtering is one of the most popular methods used in recommender systems. The idea is to recommend to the target user an item that users with similar tastes will prefer. An important goal of recommender systems is to predict the user's preferences accurately. However, prediction accuracy is not the only evaluation metric in recommender systems. In this dissertation, we will mainly deal with three other aspects of recommender systems, namely sparsity, robustness and diversification. The dissertation starts with iterative collaborative filtering to overcome sparsity issues in recommender systems. Instead of calculating the similarity matrix using sparse data only once, we iterate this process many times until convergence is achieved. To overcome the sparsity, users' ratings in dense areas are estimated first and these estimates are then used to estimate other ratings in sparse areas. Second, the robustness of recommender system is taken into consideration to detect shilling attacks in recommender systems. Some graph-based algorithms are applied in the user-user similarity graph to detect the highly correlated group, in order to get the group of fake users. Finally, we consider diversification of the types of information used to make recommendations. Specifically, geographical information, temporal information, social network information, and tag information are all aggregated in a biased random walk algorithm to make use of diversified data in multi-dimensional recommender systems.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Electrical Engineering

Files in This Item:
File Description SizeFormat 
Zhang_princeton_0181D_11067.pdf701 kBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.