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基于深度学习的组合推荐算法在电影系统中的应用
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Abstract:
如何根据不同用户需求将有效信息第一时间呈现给用户是推荐系统的一个重要研究方向。其中，基于深度学习的推荐系统不仅能解决信息冗余还能提升个性化推荐效果，引起了广泛的关注。由于单一的推荐算法往往并不能理想化地满足客户的需求，本文介绍了一种基于深度学习的组合推荐算法，对基于内容的推荐算法(CB)、矩阵分解推荐算法(MF)和Wide&Deep模型推荐算法进行优化，分别通过岭回归、增加隐式反馈信息和贝叶斯优化的优化方法，并通过转换型的组合方式构成组合推荐算法。实验结果表明，组合算法在Movielens数据集上的推荐效果优于单一模型，评估指标均较于单一模型有基本提升，能够实现精准的内容投放。
How to present effective information to users in a timely manner according to their different needs is an important research direction in recommendation systems. Among them, recommendation systems based on deep learning not only solve information redundancy but also improve personalized recommendation performance, which has attracted widespread attention. Due to the fact that a single recommendation algorithm often cannot ideally meet the needs of customers, this article introduces a combination recommendation algorithm based on deep learning, which optimizes content-based recommendation algorithm (CB), matrix factorization recommendation algorithm (MF), and Wide&Deep model recommendation algorithm. The optimization methods include ridge regression, adding implicit feedback information, and Bayesian optimization, and the combination recommendation algorithm is composed of a transformational combination. The experimental results show that the recommendation effect of the combined algorithm on Movielens dataset is better than that of the single model, and the evaluation indicators are basically improved compared with that of the single model, which can achieve accurate content delivery.
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