Personalized Recommender Systems with Multi-source Data

Pervasive applications of personalized recommendation models aim to seek a targeted advertising strategy for business development and to provide customers with personalized suggestions for products or services based on their personal experience. Conventional approaches to recommender systems, such as Collaborative Filtering (CF), use direct user ratings without considering latent features. To overcome such a limitation, we develop a recommendation strategy based on the so-called heterogeneous information networks. This method can combine two or multiple sources datasets and thus can reveal more latent associations/features between items. Compared with the well-known ‘k Nearest Neighborhood’ model and ‘Singular Value Decomposition’ approach, the new method produces a substantial higher accuracy under the commonly used measurement which is mean absolute deviation.