报告题目：Neural Collaborative Filtering (深度协同过滤)

报告人：何向南博士

单位：新加坡国立大学

报告时间：2016年12月16日（周五）下午3:00-4:00

报告地点：逸夫科教楼508会议室

报告人简介：何向南博士是新加坡国立大学计算机学院博士后研究员，致力于信息检索、数据挖掘、多媒体内容分析、机器学习等前沿领域研究，并取得丰硕的研究成果，在SIGIR、WWW、CIKM、AAAI等国际顶尖会议和TKDE、TOIS等顶尖学术期刊发表论文数十篇。何向南博士还是SIGIR、WWW、EMNLP等国际会议的程序委员会委员。

报告摘要：In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation — collaborative filtering.

Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering — the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general frame- work named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with nonlinearities, we propose to leverage a multi-layer perceptron to learn the user–item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

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