Semantic Preference-Based Personalized Recommendation on Heterogeneous Information Network

In recent years, the Internet has become an indispensable part of people's lives, and it offers increasingly comprehensive information tailored to people's personal preferences as well as commodity attribute information. Consequently, many researchers have used external information to improve recommendation technology. However, most previous studies consider only adding single relationship types, such as social networking friend-relationships. In the real world, considering multiple types of external relations can more accurately determine the reason why a user selected an item. To address this problem, in this paper, we propose a hybrid method called the semantic preference-based personalized recommendation on heterogeneous information networks (SPR), which combines user feedback scores with heterogeneous information networks. This method can improve recommendation problems by considering multiple types of external relationships. To apply the method, we first introduce a similarity measure between users based on a user's potential preferences in the meta-path and design the recommended model at the global and individual level. Finally, we perform experiments on two real-world data sets, finding that the SPR method achieves better results compared with the several widely employed and the state-of-the-art recommendation methods.