Hybrid multiple channels-based recommendations for mobile commerce

Chuen He Liou*, Duen-Ren Liu

*Corresponding author for this work

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

2 Scopus citations

Abstract

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased to conduct mobile commerce. Multichannel companies would like to develop mobile commerce but meet difficulties because of lack of knowledge about users' consumption behaviors on the new mobile channel. Typical collaborative filtering (CF) recommendations may suffer from the so-called sparsity problem because few products are browsed on the mobile Web. In this study, we propose a hybrid multiple channels method to resolve the lack of knowledge about users' consumption behaviors on the new channel and the difficulty of finding similar users due to the sparsity problem of the typical CF. Products are recommended to the new mobile channel users based on their browsing behaviors on the new mobile channel as well as consumption behaviors on the existing multiple channels according to different weights. Our experimental results show that the proposed method performs well compared to the other recommendation methods.

Original languageEnglish
Title of host publicationProceedings of the 43rd Annual Hawaii International Conference on System Sciences, HICSS-43
DOIs
StatePublished - 7 May 2010
Event43rd Annual Hawaii International Conference on System Sciences, HICSS-43 - Koloa, Kauai, HI, United States
Duration: 5 Jan 20108 Jan 2010

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference43rd Annual Hawaii International Conference on System Sciences, HICSS-43
CountryUnited States
CityKoloa, Kauai, HI
Period5/01/108/01/10

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