[1]黄剑波,陈方灵,丁友东,等.基于情感分析的个性化电影推荐[J].计算机技术与发展,2020,30(09):132-136.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 024]
　HUANG Jian-bo,CHEN Fang-ling,DING You-dong,et al.Personalized Movie Recommendation Based on Sentiment Analysis[J].,2020,30(09):132-136.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 024]
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基于情感分析的个性化电影推荐()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年09期
页码:
132-136
栏目:
应用开发研究
出版日期:
2020-09-10

文章信息/Info

Title:
Personalized Movie Recommendation Based on Sentiment Analysis
文章编号:
1673-629X(2020)09-0132-05
作者:
黄剑波; 陈方灵; 丁友东; 吴利杰
上海大学,上海 200072
Author(s):
HUANG Jian-bo; CHEN Fang-ling; DING You-dong; WU Li-jie
Shanghai University,Shanghai 200072,China
关键词:
电影推荐; 情感分析; 数据挖掘; 点击率预估; 个性化
Keywords:
movie recommendation; sentiment analysis; data mining; click-through rate estimation; personalized
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 09. 024
摘要:
随着互联网技术的飞速发展,越来越多的信息和服务充斥着网络,如何实现精准高效的推荐,已成为亟待解决的问题之一。 现有个性化电影推荐方法,将用户的历史评分作为推荐的重要依据,然而用户评分标准不一,很难挖掘出用户真正的喜好,难以形成精准推送。 因此,为了实现高质量的电影个性化推荐,挖掘用户评论的情感就变得尤为重要。 文中提出一种基于影评情感分析的个性化推荐方法,运用自然语言处理技术,挖掘用户影评情感倾向,将影评情感值与用户评分结合,共同计量用户喜好倾向。 并利用点击率预估模型预测点击率,为用户提供个性化的推荐服务。 实验结果表明,这种方法不仅有效解决了用户评分尺度不一等问题,且充分展现其个性化推荐的优越性。
Abstract:
With the rapid development of Internet technology,more and more information and services are flooding the network. How to achieve accurate and efficient recommendation has become? ? one of the urgent problems to be solved. The existing personalized movie recommendation method takes the user’s historical score as an important basis for recommendation. However,? ? ?the user rating standards are different,so it is difficult to find out the real preference of the users and form a precise push. Therefore,in order to achieve accurate movie personalized recommendation, it is especially important to tap the emotion of user comments. We propose a personalized recommendation method based on sentiment analysis of emotions. The natural language processing technology is used to explore the emotional tendency of user’s film reviews,and the emotional value of the film reviews is combined with user rating to jointly measure? the user’s preference. In addition, the click-through rate is predicted by click-through rate estimation model to provide personalized recommendation services for users. The experiment shows that the proposed method not only effectively solves the problem of different user ratings,but also fully demonstrates the superiority of its personalized recommendation.

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更新日期/Last Update: 2020-09-10