Review data has sparse problems that are insufficient to support learning more comprehensive user preferences. Focusing on this issue, this paper proposed a plug and play auxiliary network(NRSN), which can be combined with different models to improve their performance. The network mainly re-adjusts the user preference vector of the current model output by adding auxiliary information. Firstly, according to the target user, it used the aspect-attention mechanism to learn the preference of the neighboring users from the reviews of their neighbors. Then, it used the co-attention mechanism to match the neighboring users and the target users, and adjusted the new preference vector of the target users. The experiment on three public datasets result show that NRSN can not only improve the performance of existing models, but also can effectively alleviate the impact of review sparseness in "cold start" scenario.

[2020-10-05] Printed Article /doc/2019.05.0191/v2