Fetching the paper…
Reading the bibliography…
User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items.
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17
2005
Earlier work this paper cites.
Chen, J., Fang, H.r., Saad, Y.: Fast approximate knn graph construction for high dimensional data via recursive lanczos bisection. Journal of Machine Learning Research 10
2009
Earlier work this paper cites.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. pp. 249–256 (2010)
2010
Earlier work this paper cites.
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. pp. 43–52 (2015)
2015
Earlier work this paper cites.
He, R., McAuley, J.: Vbpr: visual bayesian personalized ranking from implicit feedback. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. pp. 144–150 (2016)
2016
Earlier work this paper cites.
Zhou, X., Lin, D., Ishida, T.: Evaluating reputation of web services under rating scarcity. In: 2016 IEEE International Conference on Services Computing (SCC). pp. 211–218. IEEE (2016)
2016
Earlier work this paper cites.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. pp. 173–182 (2017)
2017
Earlier work this paper cites.
Liu, Q., Wu, S., Wang, L.: Deepstyle: Learning user preferences for visual recommendation. In: Proceedings of the 40th international acm sigir conference on research and development in information retrieval. pp. 841–844 (2017)
2017
Earlier work this paper cites.
Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence 41
2018
Earlier work this paper cites.
Zhang, Z., Li, X., Gan, C.: Multimodality fusion for node classification in d2d communications. IEEE Access 6
2018
Earlier work this paper cites.
Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., Zha, H.: Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 765–774 (2019)
2019
Cited alongside, same era.
Hu, X., Yang, K., Fei, L., Wang, K.: Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In: IEEE International Conference on Image Processing. pp. 1440–1444 (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Wang, Y., Xu, X., Yu, W., Xu, R., Cao, Z., Shen, H.T.: Combine early and late fusion together: A hybrid fusion framework for image-text matching. In: 2021 IEEE International Conference on Multimedia and Expo. pp. 1–6 (2021)
2021
Later among the works it cites.
Zhang, J., Zhu, Y., Liu, Q., Wu, S., Wang, S., Wang, L.: Mining latent structures for multimedia recommendation. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 3872–3880 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Liu, Q., Yao, E., Liu, C., Zhou, X., Li, Y., Xu, M.: M2gcn: multi-modal graph convolutional network for modeling polypharmacy side effects. Applied Intelligence pp. 1–12 (2022)
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. pp. 165–174 (2019)
2019
Cited alongside, same era.
Wei, Y., Wang, X., Guan, W., Nie, L., Lin, Z., Chen, B.: Neural multimodal cooperative learning toward micro-video understanding. IEEE Transactions on Image Processing 29
2019
Cited alongside, same era.
Wei, Y., Wang, X., Nie, L., He, X., Hong, R., Chua, T.S.: Mmgcn: Multi-modal graph convolution network for personalized recommendation of micro-video. In: Proceedings of the 27th ACM International Conference on Multimedia. pp. 1437–1445 (2019)
2019
Cited alongside, same era.
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. pp. 639–648 (2020)
2020
Cited alongside, same era.
Wei, Y., Wang, X., Nie, L., He, X., Chua, T.S.: Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 3541–3549 (2020)
2020
Cited alongside, same era.
Liu, W., Duan, H., Li, Z., Liu, J., Huo, H., Fang, T.: Entity representation learning with multimodal neighbors for link prediction in knowledge graph. In: 2021 7th International Conference on Computer and Communications. pp. 1628–1634 (2021)
2021
Cited alongside, same era.
Wang, Q., Wei, Y., Yin, J., Wu, J., Song, X., Nie, L.: Dualgnn: Dual graph neural network for multimedia recommendation. IEEE Transactions on Multimedia (2021)
2021
Cited alongside, same era.
Tao, Z., Liu, X., Xia, Y., Wang, X., Yang, L., Huang, X., Chua, T.S.: Self-supervised learning for multimedia recommendation. IEEE Transactions on Multimedia (2022)
2022
Later among the works it cites.
Zhang, L., Liu, Y., Zhou, X., Miao, C., Wang, G., Tang, H.: Diffusion-based graph contrastive learning for recommendation with implicit feedback. In: Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11–14, 2022, Proceedings, Part II. pp. 232–247. Springer (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.