标题: 行动商务产品推荐方法
Product Recommendation Approaches for Mobile Commerce
作者: 刘纯和
Liou, Chuen-He
刘敦仁
Liu, Duen-Ren
资讯管理研究所
关键字: 产品推荐;行动商务;协同过滤法;手机特征法;混合多通路法;product recommendation;mobile commerce;collaborative filtering;mobile phone features;hybrid multiple channels
公开日期: 2010
摘要: 随着第三代行动通讯(3G)的用户数增加,使得行动资料传输量增大,因此促使行动商务的形成。但多通路的公司想要发展行动商务往往遇到困难,因为缺乏对新行动通路使用者消费行为的了解。而传统协同过滤推荐法因很少产品在行动网站上被浏览到所以可能产生资料稀疏的问题。

在这篇研究中,我们首先提出一个以手机特征为基础的混合推荐法去解决传统协同过滤法在行动环境下资料稀疏的问题,我们运用手机的特征去辨认使用者的偏好,然后依其特性将使用者分群。此混合推荐法结合了手机特征和产品偏好,并且运用了用户群产生关联规则来推荐产品。

接着,我们提出一个混合多通路方法去解决对于新通路使用者消费行为的未知问题,与传统协同过滤法中因找不到相似使用者所产生的资料稀疏问题。推荐给新行动通路的产品是基于新行动通路的浏览行为与既有通路的消费行为以不同的权重混合而成。

最后,我们结合了手机特征与多通路法成为一个手机特征多通路混合法,利用关联规则与最频繁项目集来推荐产品。我们的实验显示手机特征多通路混合法的推荐品质比手机特征法和混合多通路法好,亦比传统协同过滤法好。
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.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079064516
http://hdl.handle.net/11536/40274
显示于类别:Thesis


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