REHREC: Review Effected Heterogeneous Information Network Recommendation System

Abstract

Heterogeneous Information Networks have bunches of rich secret information that assist us in the creation of successful recommendation frameworks. A Heterogeneous Information Network (HIN) includes useful knowledge required for a recommendation system, and the network embedding is the common strategy for getting useful knowledge out of a HIN to be used in recommendation platforms. Although user and business nodes have been used in HINs, review contents have not been used. In this work, we use review nodes in HINs in addition to user and business nodes. Since written reviews provide valuable insights into points of interest within recommendation systems, integrating review nodes into HINs allows us to assess their impact on recommendation systems. Specifying meaningful meta-paths aids in extracting hidden information within a heterogeneous information network. While user and business nodes are typically utilized for specifying meaningful meta-paths, review nodes have been overlooked. We introduce new meta-paths incorporating review nodes to uncover hidden information in heterogeneous information networks. These meta-paths are leveraged to enhance the recommendation system’s performance. This study endeavors to amalgamate rich written reviews with heterogeneous information networks and analyze their effects on recommendation systems. Our experiments demonstrate that incorporating review texts improves the recommendation system, particularly when selecting meaningful meta-paths. Augmenting HINs with reviews facilitates the capture of additional relational information between users and businesses, thereby enhancing the recommendation model. This underscores the benefits of consolidating interaction information within HIN features for superior recommendation outcomes.

Keywords

• Heterogeneous information networks
• recommendation systems
• network embedding
• meta-path base random walk