| 摘 要: 随着互联网数据量的不断扩大，数据的实时推荐需求已经不能被传统的推荐模型所满足，协同过滤推荐算法的不足也越来越明显。为此，通过大数据计算框架Spark平台构建基于模型的推荐算法来更好地应对海量数据实时推荐的问题。首先，通过预先设定的计算方法进行模型的构建；同时将一种改进的余弦相似度算法应用到模型中，不仅可以缩短推荐实现的时间，而且可以提高推荐性能。实验结果表明，该算法和传统协同过滤算法相比，提高了准确率和时效性，验证了系统可较好地满足用户的实时需求。 |
| 关键词: Spark；实时；推荐算法；协同过滤 |
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中图分类号: TP399
文献标识码: A
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| 基金项目: 自贡市科技局科技计划(2018GYCX33). |
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| Research on Real-time Movie Recommendation Algorithm based on Big Data Platform |
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NIU Lushuai, PENG Yan
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(School of Automation and Information Engineering, Sichuan Light Chemical Technology University, Yibin 644000, China)
644056822@qq.com; 2634932795@qq.com
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| Abstract: With the continuous expansion of the amount of Internet data, traditional recommendation model can no longer meet the demand for real-time recommendation. The deficiency of collaborative filtering recommendation algorithm is becoming more and more obvious. For this reason, this paper proposes to build a model-based recommendation algorithm based on the Spark platform of big data computing framework, in order to better deal with the problem of realtime recommendation of massive data. First of all, the model is constructed through the preset calculation method, and an improved cosine similarity algorithm is applied to the model, which can not only shorten the time of recommendation implementation, but also improve the performance of recommendation. Experimental results show that compared with the traditional collaborative filtering algorithm, the proposed algorithm improves the accuracy and timeliness, and verifies that the system can better meet the real-time needs of users. |
| Keywords: Spark; real time; recommendation algorithm; collaborative filtering |
