Thesiss

Recommender systems apply numerous knowledge discovery techniques to suggest the preferred products, information, or service on contextual data. In our study, we utilize the recommender system for analyzing and measuring the network dynamics. The dynamic factors such as change i…

With the explosive growth of information on the Internet, recommender systems have been broadly applied in user engaged systems to efficiently discover items of potential interest. In order to automatically generate such “guess what you like” results and serve matching recommend…

With the continuous growth in the number of products available in e-commerce applications and information items available on the Internet, the task of associating users with a small list of personalized items, extracted from a large and diverse pool of items, is clearly beyond h…

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering…