关键词: 推荐系统, 数据稀疏, Skip-gram, 加权损失函数, 深度神经网络

Abstract:

Aiming at the problems of sparse data, low accuracy and single recommendation in the application of traditional collaborative filtering technology in network recommendation system, this paper proposes a deep neural network recommendation model named DSM combining skip-gram item embedding model and weighted loss function. Firstly, the proposed model uses three layers of ReLU to regress the output vectors, which improves the recommendation accuracy without using additional information. Secondly, this paper uses the Skip-gram model for item embedding to obtain a denser representation vector, which reduces the computation. Moreover, the weighted loss function is used to train the parameters of the deep neural network, which balances the popularity of the recommended items and ensures the novelty. Finally, the experimental results in the APP dataset and the Last.fm dataset show that the proposed model has a certain improvement in accuracy and diversity compared to existing methods when recommending applications and songs.

Key words: recommendation system, data sparse, Skip-gram, weighted loss function, deep neural network