Collaborative Filtering Recommendation Algorithm based on Spark
Collaborative Filtering Recommendation Algorithm based on Spark
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DOI：
10.23940/ijpe.19.03.p22.930938
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发表时间：
2019-03
影响因子：
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通讯作者：
Jinhong Tao;Jianhou Gan;Bin Wen
中科院分区：
文献类型：
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作者：
Jinhong Tao;Jianhou Gan;Bin Wen
随着大数据时代的到来，信息过载问题变得尤为严重。推荐系统通过分析用户的基本信息和用户的行为信息，为用户提供个性化的推荐服务。如何精准高效地推送信息，成为大数据时代迫切需要解决的问题。在交替最小二乘（ALS）协同过滤推荐算法的基础上，通过在损失函数上合并项目的相似度，减少了不可见因素项目属性信息的损失。同时，将冷启动策略引入模型中，完成推荐。该算法在Spark分布式平台和单节点上使用Group透镜实验室发布的Movie透镜数据集实现。实验结果表明，与传统推荐算法相比，该推荐算法能较好地缓解数据稀疏性问题。该算法提高了推荐的准确性和计算效率。
With the advent of the era of big data, the problem of information overload has become particularly serious. The recommendation system can provide personalized recommendation services for users by analyzing users' basic information and users' behavior information. How to push information accurately and efficiently has become an urgent issue in the era of big data. Based on the Alternating Least Squares (ALS) collaborative filtering recommendation algorithm, this paper reduces the loss of the invisible factor item attribute information by merging the similarity of the item on the loss function. At the same time, the cold start strategy is introduced into the model to complete the recommendation. The algorithm is implemented on the Spark distributed platform and single node by using the Movie Lens dataset published by the GroupLens Lab. The experiment results show that the proposed recommendation algorithm can preferably alleviate the data sparsity problem compared with the traditional recommendation algorithm. Moreover, the algorithm improves the accuracy of recommendation and the efficiency of calculation.