For better user satisfaction and business effectiveness, more and more attention has been paid to the sequence-based recommendation system, which is used to infer the evolution of users' dynamic preferences, and recent studies have noticed that the evolution of users' preferences can be better understood from the implicit and explicit feedback sequences. However, most of the existing recommendation techniques do not consider the noise contained in implicit feedback, which will lead to the biased representation of user interest and a suboptimal recommendation performance. Meanwhile, the existing methods utilize item sequence for capturing the evolution of user interest. The performance of these methods is limited by the length of the sequence, and can not effectively model the long-term interest in a long period of time. Based on this observation, we propose a novel CTR model named denoising user-aware memory network (DUMN). Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users. Extensive experiments on two real e-commerce user behavior datasets show that DUMN has a significant improvement over the state-of-the-art baselines. The code of DUMN model has been uploaded as an additional material.
翻译:为了提高用户满意度和业务效力,人们越来越注意基于序列的建议系统,该系统用来推断用户动态偏好的变化,最近的研究注意到,用户偏好的变化可以从隐含和明确的反馈序列中更好地理解,但是,大多数现有建议技术并不考虑隐含反馈中所含的噪音,这将导致用户兴趣的偏差和不最优化的建议性业绩。同时,现有方法利用项目序列来获取用户兴趣的演变。这些方法的绩效受到序列长度的限制,无法在很长的时期内有效地模拟长期利益。基于这一观察,我们提议了一个名为去掉用户觉记忆记忆网络(DDMN)的新CTR模式。具体地说,该框架:(一) 提议一个基于或图层图的地貌净化模块,该模块使用明确的反馈来净化模型反馈的表示,并有效地淡化隐含的反馈;(二) 设计一个用户记忆网络,用以模拟长期利益,以精细的egraminal-informal 改进用户的短期利益,通过改进一个基于实际记忆网络(D)的缩缩缩缩缩定义,从而忽略了现有用户的基线;通过改进现有数据模型的缩缩缩缩定义,从而忽略了现有用户的缩缩缩定义;