Two-steps graph-based collaborative filtering using user and item similarities: Case study of E-commerce recommender systems

Aghny Arisya Putra, Rahmad Mahendra, Indra Budi, Qorib Munajat

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

9 Citations (Scopus)

Abstract

Collaborative filtering has been used extensively in the commercial recommender system because of its effectiveness and ease of implementation. Collaborative filtering predicts a user's preference based on preferences of similar users or from similar items to items that are purchased by this user. The use of either user-based or item-based similarity is not sufficient. For that particular issues, hybridization of user-based and item-based in one collaborative filtering recommender system can be used to sort relevant item out of a set of candidates. This method applies similarity measures using link prediction to predict target item by combining user similarity with item similarity. The experiment results show that the combination of user and item similarities in two-steps collaborative filtering setting improves accuracy compared to the algorithm applying only user or item similarity.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Data and Software Engineering, ICoDSE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538614495
DOIs
Publication statusPublished - 7 Feb 2018
Event4th International Conference on Data and Software Engineering, ICoDSE 2017 - Palembang, Indonesia
Duration: 1 Nov 20172 Nov 2017

Publication series

NameProceedings of 2017 International Conference on Data and Software Engineering, ICoDSE 2017
Volume2018-January

Conference

Conference4th International Conference on Data and Software Engineering, ICoDSE 2017
Country/TerritoryIndonesia
CityPalembang
Period1/11/172/11/17

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