Recommender system improvement cases through implicit feedbacks from social network

Mohamad Ivan Fanany, Ibrahim Malik Khasbulloh, Azis Maarij Jamil, Muhammad K.A. Taqwim

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

1 Citation (Scopus)

Abstract

Recommender systems (RS) performance largely depends on diverse types of input that characterize users' preference in the form of both explicit and implicit feedbacks. An explicit feedback is stated directly by an explicit input from users regarding their interest in some options of services or products. Such feedback, however, is not always available. On the other hand, an implicit feedback, which reflects users' opinion indirectly through user behavior is far more abundant. In this paper, we elaborate several ways to improve the RS of three real cases dataset (online travel service, online transportation, and telecommunication service provider) through implicit feedbacks. In the first case, we analyze the effect of a simple feedback from users' input during registration without using any social network analysis (SNA). In the second case, we analyze the effect of community structure extracted from its SNA as its additional attributes. In the third case, we analyze the effect of more additional feedback attributes (modularity, PageRank, eigenvector centrality, clustering coefficient, weighted in degree, weighted outdegree, weighted degree) which also obtained from the SNA of the corresponding dataset. Given the right hyperparameter settings, we observed RS improvement in term of RMSE (root mean square error) in the three cases. In this paper, three RS models: SVD, SVD++, and difference SVD are used. Besides discussing the RS performance, we also discuss the computational cost incurred from incorporating those implicit feedbacks.

Original languageEnglish
Title of host publicationInternational Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages340-344
Number of pages5
ISBN (Electronic)9781509040315
DOIs
Publication statusPublished - 12 Dec 2017
Event8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of
Duration: 18 Oct 201720 Oct 2017

Publication series

NameInternational Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume2017-December

Conference

Conference8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period18/10/1720/10/17

Keywords

  • gradient descent
  • latent model
  • neighborhood
  • recommendation system
  • Tensorflow

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