Jiahui Shi
ABSTRACT
Friend recommendation systems in online social and professional networks such as Snapchat helps users find friends and build connections, leading to better user engagement and retention. Traditional friend recommendation systems take advantage of the principle of locality and use graph traversal to retrieve friend candidates, e.g. Friends-of-Friends (FoF). While this approach has been adopted and shown efficacy in companies with large online networks such as Linkedin and Facebook, it suffers several challenges: (i) discrete graph traversal offers limited reach in cold-start settings, (ii) it is expensive and infeasible in realtime settings beyond 1 or 2 hop requests owing to latency constraints, and (iii) it cannot well-capture the complexity of graph topology or connection strengths, forcing one to resort to other mechanisms to rank and find top-K candidates. In this paper, we proposed a new Embedding Based Retrieval (EBR) system for retrieving friend candidates, which complements the traditional FoF retrieval by retrieving candidates beyond 2-hop, and providing a natural way to rank FoF candidates. Through online A/B test, we observe statistically significant improvements in the number of friendships made with EBR as an additional retrieval source in both low- and high-density network markets. Our contributions in this work include deploying a novel retrieval system to a large-scale friend recommendation system at Snapchat, generating embeddings for billions of users using Graph Neural Networks, and building EBR infrastructure in production to support Snapchat scale.
- Sunil Arya, David M. Mount, Nathan S. Netanyahu, Ruth Silverman, and Angela Y. Wu. 1998. An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions. J. ACM, Vol. 45, 6 (nov 1998), 891--923. https://doi.org/10.1145/293347.293348Google Scholar
Digital Library
- Danah M Boyd and Nicole B Ellison. 2007. Social network sites: Definition, history, and scholarship. Journal of computer-mediated Communication, Vol. 13, 1 (2007), 210--230.Google ScholarDigital Library
- Jilin Chen, Werner Geyer, Casey Dugan, Michael Muller, and Ido Guy. 2009. Make New Friends, but Keep the Old: Recommending People on Social Networking Sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI '09). Association for Computing Machinery, New York, NY, USA, 201--210. https://doi.org/10.1145/1518701.1518735Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. New York, NY, USA.Google ScholarDigital Library
- Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, et al. 2021. Eta prediction with graph neural networks in google maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3767--3776.Google ScholarDigital Library
- David Elsweiler, Ian Ruthven, and Christopher Jones. 2007. Towards memory supporting personal information management tools. Journal of the American Society for Information Science and Technology, Vol. 58, 7 (2007), 924--946.Google ScholarDigital Library
- Alessandro Epasto and Bryan Perozzi. 2019. Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. In The World Wide Web Conference. ACM. https://doi.org/10.1145/3308558.3313660Google ScholarDigital Library
- Facebook. 2023. Where do People You May Know suggestions come from on Facebook? https://www.facebook.com/help/163810437015615Google Scholar
- Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263--1272.Google Scholar
- Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst., Vol. 6, 4, Article 13 (dec 2016), 19 pages. https://doi.org/10.1145/2843948Google ScholarDigital Library
- Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, and Tong Zhao. 2022. Linkless Link Prediction via Relational Distillation. arXiv preprint arXiv:2210.05801 (2022).Google Scholar
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017a. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017).Google Scholar
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).Google Scholar
- Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah. 2022. MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. arXiv preprint arXiv:2210.00102 (2022).Google Scholar
- Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020. Embedding-Based Retrieval in Facebook Search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD '20). Association for Computing Machinery, New York, NY, USA, 2553--2561. https://doi.org/10.1145/3394486.3403305Google ScholarDigital Library
- Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, Vol. 7, 3 (2019), 535--547.Google ScholarCross Ref
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. https://doi.org/10.48550/ARXIV.1412.6980Google ScholarCross Ref
- Jon Kleinberg. 2000. The small-world phenomenon: An algorithmic perspective. In Proceedings of the thirty-second annual ACM symposium on Theory of computing. 163--170.Google ScholarDigital Library
- Pei Lee, Laks V.S. Lakshmanan, Mitul Tiwari, and Sam Shah. 2014. Modeling Impression Discounting in Large-Scale Recommender Systems. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD '14). Association for Computing Machinery, New York, NY, USA, 1837--1846. https://doi.org/10.1145/2623330.2623356Google ScholarDigital Library
- Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting Positive and Negative Links in Online Social Networks. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW '10). Association for Computing Machinery, New York, NY, USA, 641--650. https://doi.org/10.1145/1772690.1772756Google ScholarDigital Library
- Dong Li, Ruoming Jin, Jing Gao, and Zhi Liu. 2020. On sampling top-k recommendation evaluation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2114--2124.Google ScholarDigital Library
- David Liben-Nowell and Jon Kleinberg. 2003. The Link Prediction Problem for Social Networks. In Proceedings of the Twelfth International Conference on Information and Knowledge Management (New Orleans, LA, USA) (CIKM '03). Association for Computing Machinery, New York, NY, USA, 556--559. https://doi.org/10.1145/956863.956972Google ScholarDigital Library
- Linkedin. 2023. People You May Know Feature. https://www.linkedin.com/help/linkedin/answer/a544682/people-you-may-know-feature?lang=enGoogle Scholar
- Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah. 2021. A unified view on graph neural networks as graph signal denoising. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1202--1211.Google ScholarDigital Library
- Yu A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence, Vol. 42, 4 (2018), 824--836.Google Scholar
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK. http://nlp.stanford.edu/IR-book/information-retrieval-book.htmlGoogle Scholar
- Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah. 2021. Graph neural networks for friend ranking in large-scale social platforms. In Proceedings of the Web Conference 2021. 2535--2546.Google ScholarDigital Library
- Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The Graph Neural Network Model. IEEE Transactions on Neural Networks, Vol. 20, 1 (2009), 61--80. https://doi.org/10.1109/TNN.2008.2005605Google ScholarDigital Library
- William Shiao, Zhichun Guo, Tong Zhao, Evangelos E Papalexakis, Yozen Liu, and Neil Shah. 2023. Link Prediction with Non-Contrastive Learning. ICLR (2023).Google Scholar
- Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, and Neil Shah. 2022. Friend story ranking with edge-contextual local graph convolutions. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1007--1015.Google ScholarDigital Library
- Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, and Suhang Wang. 2020. Knowing your fate: Friendship, action and temporal explanations for user engagement prediction on social apps. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2269--2279.Google ScholarDigital Library
- George Wu Tim Koh and Michael Mi. 2021. Manas HNSW Realtime: Powering Realtime Embedding-Based Retrieval. https://medium.com/pinterest-engineering/manas-hnsw-realtime-powering-realtime-embedding-based-retrieval-dc71dfd6afdd Retrieved Jan 22, 2021 fromGoogle Scholar
- Johan Ugander and Lars Backstrom. 2013. Balanced label propagation for partitioning massive graphs. In Proceedings of the sixth ACM international conference on Web search and data mining. 507--516.Google ScholarDigital Library
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, Vol. 32, 1 (2020), 4--24.Google ScholarCross Ref
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 974--983.Google ScholarDigital Library
- Guillaume Saint Jacques YinYin Yu and Paul Matsiras. 2021. How to build an effective professional network on LinkedIn: Some data-driven insights. https://engineering.linkedin.com/blog/2021/professional-network-checklist Retrieved April 2, 2021 fromGoogle Scholar
- Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931 (2019).Google Scholar
- Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah. 2021. Graph-less neural networks: Teaching old mlps new tricks via distillation. arXiv preprint arXiv:2110.08727 (2021).Google Scholar
- Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI open, Vol. 1 (2020), 57--81.Google Scholar
Index Terms
- Embedding Based Retrieval in Friend Recommendation
Recommendations
Lessons Learnt from Building Friend Recommendation Systems
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data MiningFriend recommendation systems in online social networks such as Snapchat help users find friends and build meaningful connections, leading to heightened user engagement and retention. While friend recommendation systems follow the classical ...
Friend Recommendation for Location-Based Mobile Social Networks
IMIS '13: Proceedings of the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous ComputingAlong with the rapid growth of Internet, many social websites are founded, and gradually begin to influence the people's life. Such as Facebook, the social network site provides the personalized recommendation system with friends-of-friends method to ...
Social Friend Recommendation Based on Network Correlation and Feature Co-Clustering
ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia RetrievalFriend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, friend recommendation is a difficult task and most social ...
Comments