Collaborative filtering based on user attributes and user ratings for restaurant recommendation

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2017-03-01 DOI:10.1109/IAEAC.2017.8054493

Ling Li, Ya Zhou, Han Xiong, Cailin Hu, Xiafei Wei

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引用次数: 14

Abstract

Online recommendation service had brought economic benefits for traditional catering industry. Aimed at the status quo, user-based collaborative filtering (UCF) algorithm was applied to restaurant recommendations in this paper. However, users' preference about restaurant was affected by many factors, leading traditional UCF algorithm precision was low. In order to solve this problem, three improvement were proposed. Firstly, mean score was enhanced to the calculation of similarity. Secondly, the number of common items between two users was utilized to affect the credibility of similarity, so modification factor was added to weaken the pseudo similar error. Finally, the real personal information online users registered were used to calculate the similarity based on users' attributes. The experimental results show that the modified algorithm (AdvancedCF) can improve the accuracy of the similarity calculation and provide users with more accurate restaurant recommendations.

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基于用户属性和用户评分的餐厅推荐协同过滤

在线推荐服务为传统餐饮业带来了经济效益。针对这一现状，本文将基于用户的协同过滤(user-based collaborative filtering, UCF)算法应用于餐厅推荐。然而，用户对餐厅的偏好受多种因素影响，导致传统UCF算法精度较低。为了解决这一问题，提出了三点改进措施。首先，将平均得分增强为相似度的计算;其次，利用两个用户之间的共同条目数来影响相似度的可信度，加入修正因子来减弱伪相似误差;最后，利用在线用户注册的真实个人信息计算基于用户属性的相似度。实验结果表明，改进后的算法(AdvancedCF)可以提高相似度计算的准确性，为用户提供更准确的餐厅推荐。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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