基于Map-Reduce和Spark的好友推荐系统的比较研究

2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) Pub Date : 2023-02-11 DOI:10.1109/ICITIIT57246.2023.10068723

A.M. Abhishek Sai, Gottimukkala Sahil, Boddu Sasi Sai Nadh, Kalla Likhit Sai Eswar, N. S, K. Prakash, A. Mahesh

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引用次数: 0

摘要

在网上与其他人联系是一种常见的做法，随着网络活动的增加，每天都会产生大量的数据。这种类型的数据收集可以用来在社交媒体上推荐朋友。我们使用Map Reduce和Spark来分析大量的数据。利用mapreduce和Spark实现了一个好友推荐系统。此外，我们比较了两种分布式计算技术，以确定最佳解决方案。我们发现spark计算比Hadoop Map-Reduce计算在朋友推荐系统中的速度快16倍。在时间效率方面，Spark被证明比map-reduce更有效。

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Friend Recommendation System Using Map-Reduce and Spark: A Comparison Study

Connecting with other people online is common practice and huge amounts of data are generated each day by increasing web activity. This type of data collection can be used to recommend friends on social media. We have utilized Map Reduce and Spark to analyze the vast amount of data. A Friend Recommendation system has been implemented using Map Reduce and Spark. Furthermore, we compared both Distributed Computation techniques in order to determine the optimum solution. We found that spark computation is 16 times faster than Hadoop Map-Reduce computation for Friend Recommendation System. Spark proves to be more efficient than map-reduce in terms of time efficiency.

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2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)

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