Latent Interest and Topic Mining on User-Item Bipartite Networks
Jinliang Xu, Shangguang Wang, Sen Su, Sathish Kumar, Wu Chou · 2016
Latent Factor Model (LFM) is extensively used in dealing with user-item bipartite networks in service recommendation systems. To alleviate the limitations of LFM, this papers presents a novel unsupervised learning model, Latent Interest and Topic Mining model (LITM), to automatically mine the latent user interests and item topics from user-item bipartite networks. In particular, we introduce the motivation and objectives of this bipartite network based approach, and detail the model development and optimization process of the proposed LITM. This work not only provides an efficient method for latent user interest and item topic mining, but also highlights a new way to improve the accuracy of service recommendation. Experimental studies are performed and the results validate the LITM's efficiency in model training, and its ability to provide better service recommendation performance based on user-item bipartite networks are demonstrated.