Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users provide friends' information for modeling users' interest in candidate items and help items expose to potential consumers (i.e., item attraction). However, there are two issues haven't been well-studied: Firstly, for the user interests, existing methods typically aggregate friends' information contextualized on the candidate item only, and this shallow context-aware aggregation makes them suffer from the limited friends' information. Secondly, for the item attraction, if the item's past consumers are the friends of or have a similar consumption habit to the targeted user, the item may be more attractive to the targeted user, but most existing methods neglect the relation enhanced context-aware item attraction. To address the above issues, we proposed DICER (Dual Side Deep Context-aware Modulation for SocialRecommendation). Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction. Empirical results on two real-world datasets show the effectiveness of the proposed model and further experiments are conducted to help understand how the dual context-aware modulation works.
翻译:通过利用在线社交网络平台的社会关系,社会建议对改进建议绩效十分有效。用户之间的社会关系为模拟用户对候选项目的兴趣提供了朋友信息,并帮助项目暴露给潜在消费者(即吸引项目)。然而,有两个问题尚未得到充分研究:首先,对于用户利益而言,现有方法通常只是将朋友的信息汇总到候选项目的背景中,而这种浅薄的背景认识汇总使他们受到朋友有限信息的影响。第二,对于吸引项目而言,如果项目过去的消费者是目标用户的朋友或具有类似的消费习惯,则该项目对目标用户可能更具吸引力,但大多数现有方法忽视了增强对背景的认识项目吸引力的关系。为了解决上述问题,我们提议采用“双侧深背景了解社会建议” 。具体地说,我们首先提议建立一个新颖的图表神经网络,以建模社会关系和协作关系,在高阶关系上,引入双面深环境意识调节,对目标用户来说,该项目可能更具有吸引力,但大多数现有方法忽视了增强背景意识项目吸引力的关系。为了解决上述问题,我们提议了“双向深环境实验”如何进一步展示实际实验结果。