On the Long Tail Products Recommendation using Tripartite Graph

Arlisa Yuliawati, Hamim Tohari, Rahmad Mahendra, Indra Budi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The growth of the number of e-commerce users and the items being sold become both opportunities and challenges for e-commerce marketplaces. As the existence of the long-tail phenomenon, the marketplaces need to pay attention to the high number of rarely sold items. The failure to sell these products would be a threat for some B2C e-commerce companies that apply a non-consignment sale system because the products cannot be returned to the manufacturer. Thus, it is important for the marketplace to boost the promotion of long-tail products. The objective of this study is to adapt the graph-based technique to build the recommendation system for long-tail products. The set of products, customers, and categories are represented as nodes in the tripartite graph. The Absorbing Time and Hitting Time algorithms are employed together with the Markov Random Walker to traverse the nodes in the graph. We find that using Absorbing Time achieves better accuracy than the Hitting Time for recommending long-tail products.

Original languageEnglish
Pages (from-to)816-822
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Issue number1
Publication statusPublished - 2022


  • Absorbing time
  • Hitting time
  • Long tail
  • Random walker
  • Recommender system
  • Tripartite graph


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