REHREC: Review Effected Heterogeneous Information Network Recommendation System

Khalilzadeh F., ÇİÇEKLİ İ.

IEEE Access, vol.12, pp.42751-42760, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 12
  • Publication Date: 2024
  • Doi Number: 10.1109/access.2024.3379271
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.42751-42760
  • Keywords: Heterogeneous information networks, meta-path base random walk, network embedding, recommendation systems
  • Hacettepe University Affiliated: Yes

Abstract

Heterogeneous data networks have bunches of rich secret data that assist us in the creation of successful recommendation frameworks. Specifying the meaningful meta-paths helps us to extract the hidden information that exists in a heterogeneous data network. Although user and business nodes are used to specify these meaningful meta-paths, review nodes have not been used. Furthermore, written reviews assist us to obtain informative data about a point of interest in a recommendation system. We use new meta-paths which employs review nodes in order to extract the hidden information in heterogeneous data networks. In this work, it is tried to unify rich written review texts with the heterogeneous information network and their effects in recommendation systems are analyzed. Our experiments show that the review texts improve the recommendation system when meaningful meta-paths are chosen.