璁插駭棰樼洰錛歂eural Collaborative Filtering (娣卞害鍗忓悓榪囨護(hù))
銆€銆€Speaker: Xiangnan He 錛堜綍鍚戝崡錛夛紙鏂板姞鍧″浗绔嬪ぇ瀛︼紝澶氬獟浣撴悳绱㈠疄楠屽錛屽崥澹悗錛?br />銆€銆€Abstract:
銆€銆€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 -- on the basis of implicit feedback.
銆€銆€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.
銆€銆€By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework 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 non-linearities, 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.
銆€銆€鏃墮棿錛?鏈?8鏃?涓嬪崍14:00
銆€銆€鍦扮偣錛氳禌鍗氬崡妤?05瀹?br />銆€銆€嬈㈣繋騫垮ぇ甯堢敓鍙傚姞錛?/p>
