Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

IF 2.4 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
Mukul Gupta, Pradeep Kumar, B. Bhasker
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引用次数: 6

Abstract

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related metainformation, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset.
基于隐式用户反馈的个性化物品排序:一种异构信息网络方法
在当今信息超载的数字时代,为电子商务用户生成个性化推荐是一个具有挑战性和趣味性的问题。使用二元隐式反馈向电子商务用户推荐n个最感兴趣的商品是极具挑战性的。训练数据通常非常稀疏,并且具有捕获用户行为或不行为的二进制值。由于数据的稀疏性和缺乏明确的用户偏好,基于模型和基于邻域的方法生成的推荐效果不佳。近年来,利用项目相关元信息的基于网络的项目推荐方法开始受到二元隐式反馈数据的关注。在这项工作中,我们提出了一个基于异构信息网络的推荐模型,用于使用二进制隐式反馈数据进行个性化top-N推荐。为了利用与项目相关的元信息的潜力,我们使用了元路径的概念。为了提高推荐的有效性,同时利用了商品的受欢迎程度和用户的兴趣。对网络中各种元路径进行个性化的权重学习,从二元隐式反馈数据中确定用户的内在兴趣。为了证明该模型的有效性,使用真实数据集对该模型进行了实验评估。
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来源期刊
CiteScore
4.10
自引率
33.30%
发文量
0
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