Topic Model Based Recommendation Systems Retailers

Abstract

Nowadays, sellers need very good strategy to keep their customers’ loyalty and to attract new customers to their shops. One of the important ways to accomplish this task is to present new and interesting items to their customers. In this thesis, we propose a new recommender system (RS) which recommends new items to sellers that they did not sell previously in their shop. Most of the RSs, recommend items to customers; unlike traditional RSs, proposed model is designed to suggest new items to sellers. In order to build the model we adopted generative models that are used in text mining domain. Specifically, the probabilistic latent semantic analysis (pLSA) techniqueis extendedto build the proposed RS . Several experiments are conducted using a real world dataset to validate the model. Furthermore, Collaborative Filtering (CF) method is used as a baseline algorithm to compare the performance of the proposed algorithm to state-of-the-art.Our experiments suggest that the proposed recommender system is more efficient than the pure CF algorithm for this task.