Dynamic feature weighting based on user preference sensitivity for recommender systems
Dynamic feature weighting based on user preference sensitivity for recommender systems
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DOI：
10.1016/j.knosys.2018.02.019
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发表时间：
2018-06-01
影响因子：
8.8
通讯作者：
Liu, Fei
中科院分区：
文献类型：
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作者：
Tao, Longquan;Cao, Jinli;Liu, Fei
随着信息技术的发展，信息超载问题在近十年来日益突出。为了向用户提供有用和有趣的信息，提出了推荐系统主动为用户选择可能需要的优选项。在本文中，我们关注推荐系统的个性化方面，因为我们观察到，在测量物品之间的相似性时，每个用户的维度权重应该是不同的。为了实现这一结果，我们建议将项目的维度分为数字和文本等不同的类型，然后根据推荐系统中所有用户计算这些维度的平均偏好（AP）。然后，计算每个用户在每个维度上的偏好敏感性（PS），最后将动态特征权重应用于计算。所有这些方法都是通过一个食谱推荐系统进行的实验来评估的，该系统在50多个来自不同教育背景的真实志愿者用户的基础上达到了81.0%的总体满意率。爱思唯尔B.V.版权所有
With the development of information technology, the issue of information overload has become an ever-increasing problem throughout this decade. In order to provide useful and interesting information to users, recommender systems were proposed to actively select preferable items for users who potentially desired. In this paper, we focus on personalizing aspects of recommender systems, since it is observed that the weights of the dimensions should be distinct for every user when measuring the similarities between items. To achieve this outcome, we suggest that the dimensions of items should be classified into different types such as numeric and textual, and then the average preferences (AP) should be calculated for these dimensions based on all the users in the recommender system. Then, the preference sensitivities (PS) for every dimension can be calculated for every user so that finally the dynamic feature weights can be applied in the calculations. All these aforementioned methodologies are evaluated via experiments using a Recipe Recommender System which achieves an 81.0% overall satisfaction rate on a basis of more than 50 real volunteer users from diverse educational background. Crown Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.