A Hybrid Recommendation Algorithm with Co-embedded Item Attributes and Ratings
Gaowei Xin, Jiwei Qin, Jiong Zheng · 2022
In today's recommendation algorithm field, attribute feature information of users or items is usually introduced as auxiliary information to alleviate the sparsity problem, which can achieve good recommendation results to some extent. To make better use of the auxiliary information for data compression and dimensionality reduction, autoencoder models are usually employed. However, existing autoencoder-based models require the same input and output dimensions, resulting in data information loss and poor autoencoder scalability, which ultimately leads to low recommendation line performance. To cope with this problem, we propose a hybrid collaborative recommendation algorithm with co-embedded item attributes and ratings (HCR-item). By one-hot encoding the discrete item attribute features and then fusing the rating features with the item attribute features as the input of the autoencoder, the autoencoder completes the data compression and reconstructs the rating interaction prediction matrix. The algorithm proposed in this paper integrates item attribute features and rating features together, flexibly uses different feature information, extends the feature space, and solves the problem of low recommendation performance caused by sparsity. The algorithm has been extensively experimented on several real datasets and compared with other algorithms, and the results show that HCR-item has better recommendation performance.