Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising . In this paper, we propose an enhanced representation of the target item which distills relevant information from the co-occurrence items . With the enhanced representation, CER has strongerrepresentation power for the tail items compared to the state-of-the-art ICFmethods. Extensive experiments on two public benchmarks demonstrate the effectiveness of CER. CER is a model that learns thescoring function by a deep neural network with the attentive userrepresentation and fusion of raw representation of target item as input to the deep-neuro network. It has stronger representation power for tail items than the state of the ICF methods. It is a new model that has been described as CER’s ‘CER.’ The model was designed to be used in a public benchmarking system and has been tested in a number of public benchmarks to demonstrate the strength of the CER model. It was used in two public benchmarks. The

Author(s) : Yinjiang Cai, Zeyu Cui, Shu Wu, Zhen Lei, Xibo Ma

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : cer - items - representation - public - model -

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