報(bào)告題目：Neural Collaborative Filtering (深度協(xié)同過(guò)濾)
報(bào)告人：何向南博士
單位：新加坡國(guó)立大學(xué)
報(bào)告時(shí)間：2016年12月16日（周五）下午3:00-4:00
報(bào)告地點(diǎn)：逸夫科教樓508會(huì)議室
報(bào)告人簡(jiǎn)介：何向南博士是新加坡國(guó)立大學(xué)計(jì)算機(jī)學(xué)院博士后研究員，致力于信息檢索、數(shù)據(jù)挖掘、多媒體內(nèi)容分析、機(jī)器學(xué)習(xí)等前沿領(lǐng)域研究，并取得豐碩的研究成果，在SIGIR、WWW、CIKM、AAAI等國(guó)際頂尖會(huì)議和TKDE、TOIS等頂尖學(xué)術(shù)期刊發(fā)表論文數(shù)十篇。何向南博士還是SIGIR、WWW、EMNLP等國(guó)際會(huì)議的程序委員會(huì)委員。
報(bào)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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