典型文献
Graph convolution machine for context-aware recommender system
文献摘要：
The latest advance m recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph.However,such finding is mostly restricted to the collaborative filtering(CF)scenario,where the interaction contexts are not available.In this work,we extend the advantages of graph convolutions to context-aware recom-mender system(CARS,which represents a generic type of models that can handle various side information).We propose Graph Convolution Machine(GCM),an end-to-end framework that consists of three components:an encoder,graph convo-lution(GC)layers,and a decoder.The encoder projects users,items,and contexts into embedding vectors,which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item graph.The decoder digests the refined embeddings to output the prediction score by considering the interactions among user,item,and context embeddings.We conduct experiments on three real-world datasets from Yelp and Amazon,validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.
文献关键词：
中图分类号：
作者姓名：
Jiancan WU;Xiangnan HE;Xiang WANG;Qifan WANG;Weijian CHEN;Jianxun LIAN;Xing XIE
作者机构：
School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China;Prince George's Park,National University of Singapore,Singapore 118404,Singapore;Google Research,Mountain View,CA 94043,USA;Microsoft Research Asia,Beijing 100190,China
文献出处：
引用格式：
[1]Jiancan WU;Xiangnan HE;Xiang WANG;Qifan WANG;Weijian CHEN;Jianxun LIAN;Xing XIE-.Graph convolution machine for context-aware recommender system)[J].计算机科学前沿,2022(06):77-88
A类：
mender
B类：
Graph,machine,aware,recommender,system,latest,advance,recommendation,shows,that,better,representations,can,learned,via,performing,graph,convolutions,However,such,finding,mostly,restricted,collaborative,filtering,CF,scenario,where,contexts,not,available,In,this,extend,advantages,CARS,which,represents,generic,type,models,handle,various,information,We,propose,Convolution,Machine,GCM,framework,consists,three,components,encoder,layers,decoder,projects,users,items,into,vectors,passed,embeddings,digests,refined,output,prediction,score,by,considering,interactions,among,conduct,experiments,real,world,datasets,from,Yelp,Amazon,validating,effectiveness,benefits
AB值：
0.49846
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