主要考虑了当前的session作为用户的context,然后利用了word2vec,学出用户representation和利用item形成的session的representation,所谓的diversity,就是加入了用户representation。
文献题目 | 去谷歌学术搜索 | ||||||||||
Diversifying Personalized Recommendation with User-session Context | |||||||||||
文献作者 | Liang Hu | ||||||||||
文献发表年限 | 2017 | ||||||||||
文献关键字 | |||||||||||
session-based; diversity; wide-in-wide-out; Tmall; sequential | |||||||||||
摘要描述 | |||||||||||
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods. |