Personalized Recommender Systems with Multi-source Data

Yili Wang, Tong Wu, Fei Ma*, Shengxin Zhu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages15
ISBN (Print)9783030522483
Publication statusPublished - 2020
EventScience and Information Conference, SAI 2020 - London, United Kingdom
Duration: 16 Jul 202017 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1228 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceScience and Information Conference, SAI 2020
Country/TerritoryUnited Kingdom


  • Collaborative filtering
  • Heterogeneous information networks
  • Recommender systems
  • Similarity
  • Singular value decomposition

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