A novel virtual-communicated evolution learning recommendation

Yi-Cheng Chen, Yen-Liang Chen
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Abstract

PurposeIn this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.Design/methodology/approachA novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.FindingsA novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.Originality/valueBased on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.
新颖的虚拟交流进化学习建议
目的 在这个 "信息爆炸 "的时代,推荐系统(或称推荐器)在激增的在线数字活动和电子商务中扮演着寻找有趣商品的重要角色。本文的目的是建立用户偏好演变模型,以推荐用户可能感兴趣的潜在项目。设计/方法/途径本文开发了一种新颖的推荐系统,即演变学习推荐(ELR),以精确预测用户兴趣,从而进行推荐。与之前的相关方法不同,作者整合了矩阵因式分解(MF)和循环神经网络(RNN),以有效地描述用户偏好随时间的变化。与基于累积因式分解的传统方法相比,累积因式分解可以减少计算资源的使用。此外,作者还描述了 RNN 存储单元中长期和短期效应对演化模式的意义。利用上下文意识,作者开发了一种学习模型--V-LSTM,以动态捕捉用户兴趣的演变模式。通过使用训练有素的学习模型,作者预测了用户未来的偏好并推荐了相关物品。原创性/价值基于用户和物品之间的推荐关系,作者引入了一个新概念--虚拟通信,以有效学习和估计用户和物品之间的相关性。通过将发现的用户和物品的潜在特征以进化的方式融入其中,所提出的 ELR 模型可以在 "正确 "的时间向 "正确 "的用户推荐 "正确 "的物品。此外,我们还在真实数据集上进行了大量实验,并对实验结果进行了讨论。实证结果表明,ELR 明显优于之前的推荐模型。在包括电子商务、工业零售和流媒体服务在内的真实数据集中,所提出的 ELR 在所有讨论的指标上都表现出很强的泛化能力和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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