利用近因效应改进个性化旅游推荐系统

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2021-03-12 DOI:10.26599/BDMA.2020.9020026

Paromita Nitu;Joseph Coelho;Praveen Madiraju

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引用次数: 66

摘要

基于社交媒体活动的旅行推荐系统提供了定制的兴趣地点，以适应用户特定的需求和偏好。一般来说，用户对旅行目的地的偏好会随着时间的推移而变化。在这个项目中，我们及时分析了用户的推特数据，以及他们的朋友和追随者，以了解最近的旅行兴趣。机器学习分类器识别与旅行相关的推文。旅行推文随后被用于获得个性化的旅行推荐。与大多数个性化推荐系统不同，我们提出的模型通过在模型中加入时间敏感的最近度权重来考虑用户的最新兴趣。我们提出的模型优于现有的个性化兴趣地点推荐模型，总体准确率为75.23%。

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Improvising personalized travel recommendation system with recency effects

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user's inclination towards travel destinations is subject to change over time. In this project, we have analyzed users' twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user's most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.