关键词: 推荐系统, 序列预测, Item2vec, 生成对抗网络（GAN）, 偏好特征

Abstract:

The recommendation system is designed to discover new items that the user may be interested in based on the user’s historical behavior data and generate corresponding recommendations. Most studies mine similar users according to the user’s historical behavior data, and select items from the historical data of similar users, which is not appeared in the historical data of the target user, or match similar items according to user history to achieve recommendations. However, these recommendation methods have strong dependence on the original data, and it is difficult to detect the implicit sequence relationship between different items. Therefore, a recommendation algorithm which combines the Item2vec and Generative Adversarial Networks（GAN） is proposed in this paper, which can learn the relationship between different items. And this method can find the sequence relationship in the users’ history data to learn the distribution of users’ preference. Furthermore, the users’ preference can be predicted. The experiment is performed and this method has great improvement.

Key words: recommendation system, sequence prediction, Item2vec, Generative Adversarial Networks（GAN）, preference feature