Graph convolution machine for context-aware recommender system  

作　　者：Jiancan WU Xiangnan HE Xiang WANG Qifan WANG Weijian CHEN Jianxun LIAN Xing XIE 

机构地区：[1]School of Information Science and Technology,University of Science and Technology of China,Hefei,230026,China [2]National University of Singapore,5 Prince George’s Park,Singapore,118404,Singapore [3]Google Research,Mountain View,CA,94043,USA [4]Microsoft Research Asia,Beijing,100190,China

出　　处：《Frontiers of Computer Science》2022年第6期81-92,共12页中国计算机科学前沿（英文版）

基　　金：supported by the National Key Research and Development Program of China (2020AAA0106000);the National Natural Science Foundation of China (Grant Nos.61972372,U19A2079,62121002).

摘　　要：The latest advance in 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 recommender 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 convolution(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.

关 键 词：context-aware recommender systems graph convolution 

分 类 号：O17[理学—数学]