Abstract: With the arrival of an era of big data, the data of Internet is growing at an explosive rate, and users are inundated with quite a few choices, and this phenomenon is known as information overload. As an indispensable decision-making tool, recommender systems can effectively alleviate the information overload, and have been widely applied in various scene. However, the data collected in recommender systems are often sparse, leading to a higher susceptibility of the algorithm to overfitting, which has become one of the key challenges to designing high quality personalized recommendation algorithm. Moreover, the majority of recommendation algorithms overlook the distribution of users' attention towards item characteristics, making it difficult to mine comprehensive and accurate preference information and suggest satisfactory items.
In order to effectively extract user preference information and improve the performance of recommendation results, an autoencoder recommendation algorithm fused with attention mechanism is proposed. To improve the generalization ability and memory ability of the classical encoder, the proposed algorithm first designs the corresponding feature extraction modules for the low-order and high-order features contained in the data, which are named low-order feature extraction module and high-order feature extraction module. Then, the algorithm fuses the low-order feature and high-order feature to obtain the final vector representing users' preference information through the designed attention mechanism. Finally, a decoder is used for calculating the preference rating on items of user, and generate the recommendation result based on preference ratings.
To validate the effectiveness of the proposed algorithm, we conduct an ablation study on ML-100K dataset. The experimental results demonstrate that both the low-order feature extraction module and high-order feature extraction module contribute to mining user preferences, and the feature fusion of low-order feature and high-order feature based on attention mechanism can obtain more precise preference information. Furthermore, we compare our algorithm with ItemPop, CDAE, CFGAN, and Wide&Deep, which are the classic and the state of the art models. The experimental results on ML-100K, ML-1M and Yahoo Music three datasets show that the proposed algorithm significantly improves Precision, Recall, F1 value, and normalized discounted cumulative gain (NDCG), respectively.
The proposed algorithm is applicable to Internet recommendation scenarios, which can fully mine users' preference information in data, provide users with high-quality recommendation results, improve users' satisfaction and increase product transaction volume. However, the model in this paper primarily focuses on the interaction between users and items, neglecting contextual scene information. Thus, future research can consider incorporating more user information, item information, and contextual scene information into the modeling process to further improve the performance of the proposed model. Additionally, as user preferences evolve over time and in response to environmental changes, integrating time and environmental factors should be considered a pivotal research focus in the future.

Key words: recommender system, autoencoder, attention mechanisms, collaborative filtering

摘要： 为充分获取用户的个性化信息,提高推荐算法的准确性,提出了一种融合注意力机制的自编码器推荐算法。所提算法首先针对数据中蕴含的低阶特征和高阶特征,专门设计了相应的特征提取模块,增强传统编码器的泛化能力和记忆能力,然后利用注意力机制对特征进行融合,得到关于用户偏好信息的向量表示,并通过解码器预测用户对物品的购买意愿,最终实现个性化推荐任务。在ML-100K,ML-1M和Yahoo Music三个数据集上进行实验,并与主流个性化推荐算法进行对比,本文算法在Precision,Recall,F1值和归一化折损累计增益(NDCG)四个指标上均有较大的提升。在互联网推荐场景下,本文算法能够充分挖掘出用户的偏好信息,为用户提供高质量的推荐结果即给出合理的物品购买决策建议,从而最大化满足用户需求。

关键词: 推荐算法, 自编码器, 注意力机制, 协同过滤