What do my users want? Leveraging users insights to improve recommender systems in eWOM communities

Jose Carlos Romero, Maria Olmedilla and Marie Haikel-Elsabeh ()
Additional contact information
Jose Carlos Romero: Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres, DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Maria Olmedilla: SKEMA Business School - SKEMA Business School
Marie Haikel-Elsabeh: IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]

Post-Print from HAL

Abstract: eWOM (electronic word-of-mouth) communities not only help their users to gain insights through the exchange of information about products, but also to make the right purchase decisions on the basis of other users' opinions. The vast number of reviews and ratings contain plenty of useful information and recommender systems are an effective tool for filtering them and providing users with the information they are looking for. However, traditional recommender systems use the rating as an input to recommend items, which leads to the cold-start problem and data sparsity. The aim of this paper is to reduce the undesirable outcomes caused by these problems and to optimize the predictive outcomes of the recommendations in the context of eWOM communities. To this end, we propose a hybrid recommender system that combines Social and eWOM variables as an input and uses the Kmeans algorithm for dimensionality reduction and the collaborative filtering SVD++ algorithm to optimize the accuracy of recommendations. Our results show that recommender systems based on users' behavioral data from eWOM communities improve recommendations compared to other recommender systems that use different variables as an input and PCA as a dimensionality reduction technique.

Keywords: eWOM communities; Recommender system; Collaborative filtering; Kmeans; SVD+ (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Published in IEEE Engineering Management Review, inPress, pp.1-13. ⟨10.1109/EMR.2024.3428447⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04662547

DOI: 10.1109/EMR.2024.3428447

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().