Computer Sciences Commons^™

Enhance Nmf-Based Recommendation Systems With Auxiliary Information Imputation, Fatemah Alghamedy

Theses and Dissertations--Computer Science

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user's opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called "cold-start" issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating.

The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation …

Data Privacy Preservation In Collaborative Filtering Based Recommender Systems, Xiwei Wang

Theses and Dissertations--Computer Science

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives.

The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy.

It is apparent that with more customers' preference data, recommender systems …