Abstract

Recommender systems are special algorithms that allow users to receive personalized recommendations on topics that interest them. Systems of this kind are widely used in various fields, for example, in e-commerce, provider services, social networks, etc. Together with classical approaches, neural networks have also become popular in recommender systems in recent years, which are gradually replacing traditional methods of collaborative filtering and content-based algorithms. However, neural networks require large computing resources, which often raises questions on whether an increase in quality will be justified and whether there be one at all. The neural network approach in recommender systems—the self-attentive sequential recommendation (SASRec) transformer model from Microsoft Recommenders—is studied and compared with the classic algorithm, the LightFM hybrid model. For training and validation, the data taken from a housing search application are used. It is proposed to use the hit rate as the main metric for comparison. The results of the experiments will help to understand which algorithms have higher accuracy in terms of predictions and recommendations. As an additional part, the clustering of user and object embeddings is considered.

中文翻译：

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推荐系统的神经网络方法

摘要

推荐系统是特殊的算法，允许用户接收有关他们感兴趣的主题的个性化推荐。此类系统广泛应用于各个领域，例如电子商务、提供商服务、社交网络等。近年来，神经网络与经典方法一起在推荐系统中也开始流行，并逐渐取代传统的推荐系统。协同过滤方法和基于内容的算法。然而，神经网络需要大量的计算资源，这常常引发这样的问题：质量的提高是否合理以及是否存在。研究了推荐系统中的神经网络方法（来自 Microsoft Recommenders 的自注意力顺序推荐 (SASRec) 变压器模型），并与经典算法 LightFM 混合模型进行了比较。为了进行训练和验证，使用从住房搜索应用程序获取的数据。建议以命中率作为主要衡量指标进行比较。实验结果将有助于了解哪些算法在预测和建议方面具有更高的准确性。作为附加部分，考虑了用户和对象嵌入的聚类。