{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-collaborative-filtering","title":"Neural Collaborative Filtering","arxiv_id":"1708.05031","date":"2017-08-16","proceeding":"WWW 2017 4","authors":["Xiangnan He","Lizi Liao","Hanwang Zhang","Liqiang Nie","Xia Hu","Tat-Seng Chua"],"abstract":"In recent years, deep neural networks have yielded immense success on speech\nrecognition, computer vision and natural language processing. However, the\nexploration of deep neural networks on recommender systems has received\nrelatively less scrutiny. In this work, we strive to develop techniques based\non neural networks to tackle the key problem in recommendation -- collaborative\nfiltering -- on the basis of implicit feedback. Although some recent work has\nemployed deep learning for recommendation, they primarily used it to model\nauxiliary information, such as textual descriptions of items and acoustic\nfeatures of musics. When it comes to model the key factor in collaborative\nfiltering -- the interaction between user and item features, they still\nresorted to matrix factorization and applied an inner product on the latent\nfeatures of users and items. By replacing the inner product with a neural\narchitecture that can learn an arbitrary function from data, we present a\ngeneral framework named NCF, short for Neural network-based Collaborative\nFiltering. NCF is generic and can express and generalize matrix factorization\nunder its framework. To supercharge NCF modelling with non-linearities, we\npropose to leverage a multi-layer perceptron to learn the user-item interaction\nfunction. Extensive experiments on two real-world datasets show significant\nimprovements of our proposed NCF framework over the state-of-the-art methods.\nEmpirical evidence shows that using deeper layers of neural networks offers\nbetter recommendation performance.","url_abs":"http://arxiv.org/abs/1708.05031v2","url_pdf":"http://arxiv.org/pdf/1708.05031v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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