Predicting product purchases from transaction data using aspect models | |
| Author | Ha Hong Thuy |
| Call Number | AIT Thesis no.IM-03-10 |
| Subject(s) | Data mining Barter Mathematical models |
| Note | A thesis submitted in pa11ial fulfillment of the requirements for the degree of Master of Science, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. IM-03-10 |
| Abstract | Predicting product purchases is one of the important tasks of a broker in the barter trade exchange. This work introduces the utilization of aspect models - a latent class statistical mixture model used for soft-clustering of co-occurrence data - for generating future purchase predictions for existing and new members in a barter exchange from transaction data. Three aspect models are investigated. Expectation Maximization (EM) algorithm and Annealed Expectation Maximization algorithm are used to fit the models with the data. A system is implemented to train and evaluate the performance of the proposed models and algorithms. Several experiments are carried out to determine the optimal number of states for aspects for each model and to determine which model performs better. The experimental results show that aspect models work well in predicting product purchases from transaction data. |
| Year | 2003 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. IM-03-10 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Haddawy, Peter; |
| Examination Committee(s) | Vatcharapom Esichaikul;Guha, Sumanta ; |
| Scholarship Donor(s) | Vietnamese Ministry of Education and Training; |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2003 |