Product Recommendation Approaches for Mobile Commerce
|關鍵字:||產品推薦;行動商務;協同過濾法;手機特徵法;混合多通路法;product recommendation;mobile commerce;collaborative filtering;mobile phone features;hybrid multiple channels|
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 first propose a mobile phone feature-based (MPF) hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users’ characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF-based method and a preference-based method that employs association rule mining to extract recommendation rules from user groups and make recommendations. Second, we propose a hybrid multiple channels (HMC) 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 typical CF. Products are recommended to the new mobile channel users based on their browsing behaviors on the new mobile channel as well as the consumption behaviors on the existing multiple channels according to different weights. Finally, we combine MPF with HMC approach into a hybrid MPF-HMC method, which utilizes association rules of product categories and products as well as most frequent items to recommend products. Our experiment results show that the hybrid MPF-HMC combined method performs well compared to the pure MPF-based and HMC-based methods as well as the typical kNN-based CF method.
|Appears in Collections:||Thesis|
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