research-article

PYMK: friend recommendation at myspace

Authors:

Michael Moricz,

Yerbolat Dosbayev,

Mikhail BerlyantAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 999 - 1002

https://doi.org/10.1145/1807167.1807276

Published: 06 June 2010 Publication History

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Abstract

In recent years Social Networking has enjoyed a significant increase in popularity. The main reason behind this surge in popularity is the social experience associated with connecting content to people and also connecting people with other people. Knowing, seeing, hearing what our friends and like-minded people feel or listen to or upload is an unparalleled experience. Similar to real life, finding good friends is not easy without the help of good recommendations. In this Industry Talk paper we present the MySpace friend recommendation algorithm named People You May Know. We will also comment on both the quality and the effectiveness of the algorithms.

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He YJia ZHu MZhang GDong H(2023)The Hybrid Trip Destination Prediction Model of Vehicles Based on Autoencoder and High-Order Interaction FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.312537224:8(8443-8451)Online publication date: Aug-2023
https://doi.org/10.1109/TITS.2021.3125372
Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Roy AMofakh Kharul Islam A(2023)Friend Recommendation System Based on Heterogeneous Data from Social NetworkProceedings of International Joint Conference on Advances in Computational Intelligence10.1007/978-981-99-1435-7_47(565-580)Online publication date: 16-Jun-2023
https://doi.org/10.1007/978-981-99-1435-7_47
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Index Terms

PYMK: friend recommendation at myspace
1. Information systems
  1. Information retrieval
    1. Document representation

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Information

Published In

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2010

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SIGMOD/PODS '10

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SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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He YJia ZHu MZhang GDong H(2023)The Hybrid Trip Destination Prediction Model of Vehicles Based on Autoencoder and High-Order Interaction FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.312537224:8(8443-8451)Online publication date: Aug-2023
https://doi.org/10.1109/TITS.2021.3125372
Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Roy AMofakh Kharul Islam A(2023)Friend Recommendation System Based on Heterogeneous Data from Social NetworkProceedings of International Joint Conference on Advances in Computational Intelligence10.1007/978-981-99-1435-7_47(565-580)Online publication date: 16-Jun-2023
https://doi.org/10.1007/978-981-99-1435-7_47
Tong MTao YZhang YBian KYan W(2022)Trajectory-Based User Encounter Prediction Over Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-019-06367-1107:4(1933-1949)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s11277-019-06367-1
Li WXu ZSun YGong QChen YDing AWang XHui P(2021)DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable FeaturesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3091503(1-1)Online publication date: 2021
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Cui LWu JPi DZhang PKennedy P(2020)Dual Implicit Mining-Based Latent Friend RecommendationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.277788950:5(1663-1678)Online publication date: May-2020
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Chen YHu JXiao YLi XHui P(2020)Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global ScaleIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29922947:4(1019-1032)Online publication date: Aug-2020
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Sun TTaylor S(2020)Displaying things in common to encourage friendship formation: A large randomized field experimentQuantitative Marketing and Economics10.1007/s11129-020-09224-918:3(237-271)Online publication date: 23-May-2020
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Bai GLien CChen H(2019)Co-learning Multiple Browsing Tendencies of a User by Matrix Factorization-based Multitask LearningIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352526(253-257)Online publication date: 14-Oct-2019
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Vahedian FBurke R(2018)Recommender Systems Based on Social NetworksEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110163(2081-2095)Online publication date: 12-Jun-2018
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