An item orientated recommendation algorithm from the multi-view perspective

作者：Hu Qi Ying; Zhao Zhi Lin; Wang Chang Dong^*; Lai Jian Huang

来源：Neurocomputing, 2017, 269: 261-272.

DOI：10.1016/j.neucom.2016.12.102

• Recommendation algorithm
• Item orientated
• Multi-view learning

In the traditional recommendation algorithms, items are recommended to users on the basis of users' preferences to improve selling efficiency, which however cannot always raise revenues for manufacturers of particular items. Assume that, a manufacturer has a limited budget for an item's advertisement, with this budget, it is only possible for him to market this item to limited users. How to select the most suitable users that will increase advertisement revenue? It seems to be an insurmountable problem to the existing recommendation algorithms. To address this issue, a new item orientated recommendation algorithm from the multi-view perspective is proposed in this paper. Different from the existing recommendation algorithms, this model provides the target items with the users that are the most possible to purchase them. The basic idea is to simultaneously calculate the relationships between items and the rating differences between users from a multi-view model in which the purchasing records of each user are regarded as a view and each record is seen as a node in a view. The experimental results show that our proposed method outperforms the state-of-the-art methods in the scenario of item orientated recommendation.

• 出版日期2017-12-20
• 单位中山大学