DOCSLIB.ORG

Personalized Recommendation in Social Network Sites A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Personalized Recommendation in Social Network Sites A Personalized Recommendation in Social Network Sites A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Jilin Chen IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Joseph Konstan and John Riedl, advisors September, 2011 © Jilin Chen 2011 Acknowledgements I would like to thank: • Joseph Konstan and John Riedl, for their sagacious guidance and advice • GroupLens, for providing me a friendly and intellectually rich environment • Ed Chi, for his supportive mentorship and rich intellectual contribution • Researchers and fellow interns at IBM Research and Palo Alto Research Center during my internships, for their insightful input and generous friendship • My family, for their care, support, and encouragement i Abstract Social network sites have experienced an explosion in both the number of users and the amount of user-contributed content in recent years. Today, millions of people visit Facebook, Twitter and the like to keep up with friends, to engage in random chatter, and to share and consume photos, news, useful tips and fun stories. Many active users of social network sites are, however, constantly troubled by information overload – there are too many other people to interact with and too much content to read. As a result, the difficulty in finding the right people and content to focus on has been identified as a key challenge for social network sites. In this thesis we seek to meet the challenge by designing three personalized recommender systems. The first system is a people recommender, which helps users find potential friends to connect with on social network sites; the second system is an information recommender, which helps users find interesting pieces of information from their online social network; the third system is a conversation recommender, which helps users find interesting conversations happening around their online social network. In designing the recommender systems, we adapt algorithms from related domains and create new algorithms. We refine these algorithms through offline data analysis. We then design and improve user interfaces of the recommender systems through participatory design. With the full recommender systems designed and implemented, we deploy these systems to real users of social network sites, and evaluate a variety of recommender algorithms online through user studies. Through these user studies we not only provide qualitative and quantitative comparisons of recommender algorithms, but also provide broader insights for designing recommender systems on social network sites. ii Table of Contents List of Tables...................................................................................................................v List of Figures ................................................................................................................vi Chapter 1: Introduction....................................................................................................1 1.1 Research Methodology...........................................................................................4 1.2 Thesis Overview ....................................................................................................6 Chapter 2: Related Recommendation Techniques ............................................................8 2.1 Recommendation Techniques: An Overview..........................................................8 2.2 Recommenders and Intelligent Interfaces on Social Network Sites.......................10 2.3 Recommendation Techniques in this Work ..........................................................11 Chapter 3: Recommending People .................................................................................14 3.1 Background and Research Goals ..........................................................................14 3.2 The IBM Beehive Platform ..................................................................................17 3.3 Recommender Algorithms ...................................................................................19 3.3.1 Algorithm 1: Content Matching.....................................................................19 3.3.2 Algorithm 2: Content-plus-Link (CplusL) .....................................................21 3.3.3 Algorithm 3: Friend-of-Friend (FoF) .............................................................22 3.3.4 Algorithm 4: SONAR....................................................................................23 3.4 Study I: Personalized Survey................................................................................24 3.4.1 Methodology.................................................................................................24 3.4.2 Results ..........................................................................................................27 3.5 Study II: Controlled Field Study...........................................................................34 3.5.1 Methodology.................................................................................................35 3.5.2 Results ..........................................................................................................36 3.6 Discussion ...........................................................................................................39 3.6.1 Understanding Algorithm Effectiveness ........................................................39 3.6.2 People Recommendation beyond Beehive .....................................................40 Chapter 4: Recommending Information .........................................................................42 4.1 Background and Research Goals ..........................................................................42 4.2 Information Production and Gathering on Twitter ................................................45 4.3 Recommender Design..........................................................................................46 4.3.1 Dimension 1: Selecting the Candidate Set .....................................................47 4.3.2 Dimension 2: Ranking URLs Using Topic Relevance....................................49 4.3.3 Dimension 3: Ranking URLs Using Social Voting ........................................52 4.3.4 Recommender Algorithms.............................................................................53 4.4 Pilot Interviews....................................................................................................55 4.4.1 Methodology.................................................................................................55 4.4.2 Results ..........................................................................................................57 4.5 Controlled Field Study.........................................................................................59 4.5.1 Methodology.................................................................................................60 4.5.2 Results ..........................................................................................................61 4.6 Discussion ...........................................................................................................66 4.6.1 Understanding the Effectiveness of Our Approach ........................................66 iii 4.6.2 Generalizability of Our Approach..................................................................67 4.6.3 Distinguishing Algorithms: Relevance vs. Serendipity ..................................68 Chapter 5: Recommending Conversation.......................................................................72 5.1 Background and Research Goals ..........................................................................72 5.2 Conversations on Twitter .....................................................................................74 5.3 Recommender Design..........................................................................................76 5.3.1 Factor 1: Thread Length ................................................................................77 5.3.2 Factor 2: Topic Relevance.............................................................................78 5.3.3 Factor 3: Tie-Strength ...................................................................................78 5.3.4 Recommender Algorithms.............................................................................80 5.3.5 Preliminary Explorations...............................................................................81 5.4 User Study...........................................................................................................83 5.4.1 Pre-Survey ....................................................................................................83 5.4.2 Main Study....................................................................................................83 5.4.3 Post-Survey...................................................................................................84 5.5 Results.................................................................................................................86 5.5.1 Pre-Survey ....................................................................................................86 5.5.2 Main Study....................................................................................................90 5.5.3 Post-Survey...................................................................................................93 5.5.4 Summary of Results ......................................................................................95
Load more

Recommended publications
  • Out-Of-School Factors and School Success
    Poverty and Potential: Out-of-School Factors and School Success David C. Berliner Regents’ Professor Arizona State University March 2009 The Great Lakes Center for Education Research & Practice PO Box 1263 East Lansing, MI 48826 Phone: (517) 203-2940 Email: [email protected] Web Site: http://www.greatlakescenter.org One of a series of Policy Briefs made possible by funding from the Great Lakes Center for Education Research and Practice. This policy brief was peer reviewed by members of the EPIC/EPRU Editorial Review Board. For information on the board and its members, visit: http://epicpolicy.org/editorial-board. Poverty and Potential: Out-of-School Factors and School Success David C. Berliner Arizona State University Executive Summary The U.S. has set as a national goal the narrowing of the achievement gap between lower income and middle-class students, and that between racial and ethnic groups. This is a key purpose of the No Child Left Behind act, which relies primarily on assessment to promote changes within schools to accomplish that goal. However, out-of-school factors (OSFs) play a powerful role in generating existing achievement gaps, and if these factors are not attended to with equal vigor, our national aspirations will be thwarted. This brief details six OSFs common among the poor that significantly affect the health and learning opportunities of children, and accordingly limit what schools can accomplish on their own: (1) low birth-weight and non-genetic prenatal influences on children; (2) inadequate medical, dental, and vision care, often a result of inadequate or no medical insurance; (3) food insecurity; (4) environmental pollutants; (5) family relations and family stress; and (6) neighborhood characteristics.
    [Show full text]
  • Social Influence in Social Advertising: Evidence from Field Experiments
    Social Influence in Social Advertising: Evidence from Field Experiments EYTAN BAKSHYy, Facebook DEAN ECKLESy, Stanford University & Facebook RONG YAN, Facebook ITAMAR ROSENN, Facebook Social advertising uses information about consumers’ peers, including peer affiliations with a brand, prod- uct, organization, etc., to target ads and contextualize their display. This approach can increase ad efficacy for two main reasons: peers’ affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i.e., peers’ association with a brand) alongside ads affect responses via social influence processes. For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are affiliated with peers who are strong, rather than weak, ties. We conduct two very large field experiments that identify the effect of social cues on consumer responses to ads, measured in terms of ad clicks and the formation of connections with the advertised entity. In the first experiment, we randomize the number of social cues present in word-of-mouth advertising, and measure how responses increase as a function of the number of cues. The second experiment examines the effect of augmenting traditional ad units with a minimal social cue (i.e., displaying a peer’s affiliation below an ad in light grey text). On average, this cue causes significant increases in ad performance. Using a measurement of tie strength based on the total amount of communication between subjects and their peers, we show that these influence effects are greatest for strong ties. Our work has implications for ad optimization, user interface design, and central questions in social science research.
    [Show full text]
  • Pega Customer Service Reference Guide Iii
    Pega Customer Service 7.1.4 Version REFERENCE GUIDE © Copyright 2015 Pegasystems Inc., Cambridge, MA All rights reserved. Trademarks For Pegasystems Inc. trademarks and registered trademarks, all rights reserved. Other brand or product names are trademarks of their respective holders. Consult the 718ThirdPartyLicense.pdf file on the installation media for information about the software delivered with the product. Notices This publication describes and/or represents products and services of Pegasystems Inc. It may contain trade secrets and proprietary information that are protected by various federal, state, and international laws, and distributed under licenses restricting their use, copying, modification, distribution, or transmittal in any form without prior written authorization of Pegasystems Inc. This publication is current as of the date of publication only. Changes to the publication may be made from time to time at the discretion of Pegasystems Inc. This publication remains the property of Pegasystems Inc. and must be returned to it upon request. This publication does not imply any commitment to offer or deliver the products or services described herein. This publication may include references to Pegasystems Inc. product features that have not been licensed by you or your company. If you have questions about whether a particular capability is included in your installation, please consult your Pegasystems Inc. services consultant. Although Pegasystems Inc. strives for accuracy in its publications, any publication may contain inaccuracies or typographical errors, as well as technical inaccuracies. Pegasystems Inc. may make improvements and/or changes to the publication at any time. Any references in this publication to non-Pegasystems websites are provided for convenience only and do not serve as an endorsement of these websites.
    [Show full text]
  • Early Life Influences on Emotional Reactivity
    Neuroscience and Biobehavioral Reviews 34 (2010) 808–820 Contents lists available at ScienceDirect Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev Review Early life influences on emotional reactivity: Evidence that social enrichment has greater effects than handling on anxiety-like behaviors, neuroendocrine responses to stress and central BDNF levels Francesca Cirulli a,∗, Alessandra Berry a, Luca Tommaso Bonsignore a, Francesca Capone a, Ivana D’Andrea a, Luigi Aloe b, Igor Branchi a, Enrico Alleva a a Section of Behavioural Neuroscience, Department of Cell Biology and Neuroscience, Istituto Superiore di Sanità, Viale Regina Elena 299, I-00161, Rome, Italy b Institute of Neurobiology and Molecular Medicine, CNR, Rome, Italy article info abstract Keywords: During the early post-natal phases the brain is experience-seeking and provided by a considerable plas- Handling ticity which allows a fine tuning between the external environment and the developing organism. Since Communal rearing the early work of Seymour Levine, an impressive amount of research has clearly shown that stressful Corticosterone experiences exert powerful effects on the brain and body development. These effects can last throughout Behavior the entire life span influencing brain function and increasing the risk for depression and anxiety disor- Ultrasonic vocalizations Depression ders. The mechanisms underlying the effects of early stress on the developing organism have been widely Anxiety studied in rodents through experimental
    [Show full text]
  • Deliverable D4.3: Navigation Demonstrator
    TERESA - 611153 - FP7/2013-2016 Deliverable D4.3: Navigation Demonstrator Project Acronym: TERESA Project Full Title: Telepresence Reinforcement-Learning Social Agent Grant Agreement no. 611153 Due date: M36: November 2016 Delivery: November 30, 2016 Lead partner: UPO Dissemination level: Public Status: Submitted Version: v2.0 DOCUMENT INFO Date and Version Number Author Comments 01.11.2016 v0.1 Luis Merino Scheme 10.11.2016 v0.2 Noe Perez Macro actions and social nav- igation 14.11.2016 v0.3 Rafael Ramon Approach people section 22.11.2016 v0.4 Jesus Capitan Walking side by side section 25.11.2016 v1.0 UPO team First draft 28.11.2016 v1.1 Joao Messias Revision 30.11.2016 v2.0 UPO team Submitted Contents 1 Contributors . .7 2 Executive summary . .8 3 TERESA human-aware navigation stack . .9 3.1 Introduction . .9 3.2 Robot sensors for navigation . 10 3.3 The navigation stack architecture . 11 3.3.1 Behavior manager . 12 3.3.2 Architecture of navigation behaviors . 13 4 Social waypoint navigation . 16 4.1 Introduction . 16 4.2 Path planning . 16 4.3 Low-level control . 17 4.4 Learning social navigation . 17 4.4.1 Learning a RRT* cost function . 18 4.4.2 Features for social navigation . 21 4.4.3 Experimental results . 23 4.5 Navigation evaluation . 25 4.5.1 Benchmarking according to ERL-SR . 25 4.5.2 Social evaluation . 27 5 Yield........................................... 31 6 Approaching people . 33 6.1 Introduction . 33 6.2 GMMs for interaction modeling . 34 6.3 The reproduction planner .
    [Show full text]
  • Understanding Individuals' Attachment to Social Networking Sites
    Understanding Individuals’ Attachment to Social Networking Sites: An Empirical Investigation of Three Theories by Eric T. K. Lim M.Sc. (Information Systems), National University of Singapore, 2007 B.Comm. (Hons.), Nanyang Technological University, 2003 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Segal Graduate School Beedie School of Business Eric T. K. Lim 2013 SIMON FRASER UNIVERSITY Spring 2013 Approval Name: Eric T. K. Lim Degree: Doctor of Philosophy (Business Administration) Title of Thesis: Understanding Individuals’ Attachment to Social Networking Sites: An Empirical Investigation of Three Theories Examining Committee: Chair: Dr. Tom Lawrence Professor, Academic Director, PhD Program Dr. Dianne Cyr Senior Supervisor Professor Dr. Andrew Gemino Co-Supervisor Professor Dr. Leyland Pitt Internal Examiner Professor Dr. Matthew Lee External Examiner Chair Professor Department of Information Systems College of Business City University of Hong Kong Date Defended/Approved: March 27, 2013 ii Partial Copyright Licence iii Ethics Statement iv Abstract Social Networking Sites (SNSs) are a pervasive phenomenon in today’s society. With greater connectivity and interactivity enabled via web technologies, SNSs provide communication platforms for individuals to bridge geographical and temporal differences when making friends, sharing experiences, socializing with others and much more. This thesis therefore endeavors to shed light on this problem by decomposing members’ motives for participating within SNSs into identity-based, bond-based and comparison- based attachments. Each of these forms of attachment in turn affects members’ cooperative and competitive mentality towards participation within SNSs. In addition, it is further posited in this thesis that members’ identity-based, bond-based and comparison- based attachment within SNSs can be induced through the presence of deindividuation, personalization and tournament technologies respectively.
    [Show full text]
  • The Role of Migration in the Reproduction of Social Advantage
    Belgeo Revue belge de géographie 4 | 2010 International student mobility and migration in Europe Failing to succeed ? The role of migration in the reproduction of social advantage amongst young graduates in Hong Kong Le rôle des migrations dans la reproduction des avantages sociaux chez les jeunes diplômés à Hong Kong Johanna L. Waters Electronic version URL: http://journals.openedition.org/belgeo/6419 DOI: 10.4000/belgeo.6419 ISSN: 2294-9135 Publisher: National Committee of Geography of Belgium, Société Royale Belge de Géographie Printed version Date of publication: 31 December 2010 Number of pages: 383-393 ISSN: 1377-2368 Electronic reference Johanna L. Waters, “Failing to succeed ? The role of migration in the reproduction of social advantage amongst young graduates in Hong Kong”, Belgeo [Online], 4 | 2010, Online since 15 December 2012, connection on 11 February 2021. URL: http://journals.openedition.org/belgeo/6419 ; DOI: https:// doi.org/10.4000/belgeo.6419 This text was automatically generated on 11 February 2021. Belgeo est mis à disposition selon les termes de la licence Creative Commons Attribution 4.0 International. Failing to succeed ? The role of migration in the reproduction of social adva... 1 Failing to succeed ? The role of migration in the reproduction of social advantage amongst young graduates in Hong Kong Le rôle des migrations dans la reproduction des avantages sociaux chez les jeunes diplômés à Hong Kong Johanna L. Waters Introduction “Employers generally prefer graduates who have international experience.” (British Council, 2009, Guide to UK qualifications in Hong Kong, p. 6) 1 Migrants’ personal narratives are very often marked, in one way or another, by failure.
    [Show full text]
  • How Do People Display Social Networks in Everyday Life (That Is, Not Online)? Give 2 Concrete, Specific Examples
    How do people display social networks in everyday life (that is, not online)? Give 2 concrete, specific examples. Why do they do this? Looking at this display as a signal, what is the quality it is inferring? What are the costs of making this signal? The benefits? Is there a cost to the receiver if it is not honest? One way people display social networks to others is by dropping names. For example, someone could say “I was talking to Alex Rodriguez the other day…” In these cases, signaling this information is meant to either attach oneself to a respected other or signal importance outright. While there are few costs associated with the actual creation of such a signal, the benefits normally involve an elevated level of respect (“Wow, Bob must be cool if he knows A-Rod!”). If the signal is discovered to be false, however, there could be severe social repercussions ranging from a public ridiculing to ostracism. From the receiver’s point of view, they may stand to be taken advantage of by the deceptive signaler in the form of underserved respect that may be abused by the signaler. Another display situation is when individuals use their social connections to help their friends. If someone gets their friend a job at their company they are displaying their social connections both to their friend and employer. Not only does this have the benefit of helping a friend, but it increases their standing with their employer, especially if the friend turns out to be a good employee. This action requires some amount of social capital, since the recommender is effectively extending their reputation to encompass their friend, and this reputation could be severely harmed if the friend does not work out as an employee.
    [Show full text]
  • Living and Learning with New Media: Summary of Findings from the Digital Youth Project
    The John D. and Catherine T. MacArthur Foundation Reports on Digital Media and Learning | November 2008 Living and Learning with New Media: Summary of Findings from the Digital Youth Project Mizuko Ito, Heather Horst Matteo Bittanti, danah boyd, Becky Herr-Stephenson, Patricia G. Lange, C.J. Pascoe, and Laura Robinson with Sonja Baumer, Rachel Cody, Dilan Mahendran, Katynka Martínez, Dan Perkel, Christo Sims, and Lisa Tripp www.macfound.org Living and Learning with New Media | The MacArthur Foundation 1 Building the emerging field of digital media and learning The MacArthur Foundation launched its five-year, $50 million digital media and learning initiative in 2006 to help determine how digital media are changing the way young people learn, play, socialize, and participate in civic life. Answers are critical to developing educational and other social institutions that can meet the needs of this and future generations. The initiative is both marshaling what it is already known about the field and seeding innovation for continued growth. For more information, visit www.digitallearning.macfound.org. To engage in conver- sations about these projects and the field of digital learning, visit the Spotlight blog at spotlight.macfound.org. About the MacArthur Foundation The John D. and Catherine T. MacArthur Foundation supports creative people and effective institutions committed to building a more just, verdant, and peaceful world. In addition to selecting the MacArthur Fellows, the Foundation works to defend human rights, advance global conservation and security, make cities bet- ter places, and understand how technology is affecting children and society. For more information or to sign up for MacArthur’s monthly electronic newsletter, visit www.macfound.org.
    [Show full text]
  • Technical Design of Open Social Web for Crowdsourced Democracy
    Project no. 610349 D-CENT Decentralised Citizens ENgagement Technologies Specific Targeted Research Project Collective Awareness Platforms D4.3 Technical Design of Open Social Web for Crowdsourced Democracy Version Number: 1 Lead beneficiary: OKF Due Date: 31 October 2014 Author(s): Pablo Aragón, Francesca Bria, Primavera de Filippi, Harry Halpin, Jaakko Korhonen, David Laniado, Smári McCarthy, Javier Toret Medina, Sander van der Waal Editors and reviewers: Robert Bjarnason, Joonas Pekkanen, Denis Roio, Guido Vilariño Dissemination level: PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services) Approved by: Francesca Bria Date: 31 October 2014 This report is currently awaiting approval from the EC and cannot be not considered to be a final version. FP7 – CAPS - 2013 D-CENT D4.3 Technical Design of Open Social Web for Crowdsourced Democracy Contents 1 Executive Summary ........................................................................................................................................................ 6 Description of the D-CENT Open Democracy pilots ............................................................................................. 8 Description of the lean development process .......................................................................................................... 10 Hypotheses statements
    [Show full text]
  • Demystifying Social Media to Tweet Or Not to Tweet?
    Demystifying Social Media To tweet or not to tweet? Rebecca Vander Linde Manager of Digital Communications American University University Communications and Marketing | American University 2 Facebook Live • Choose a topic that is relevant to the conversation. • Strong wi-fi connection is necessary. • Fully charged phone or connect to a power source. • Use a tripod. • Think about sound • Lavalier mics and a splitter. • Choose a static background and frame your shot. • Interact with commenters, if necessary. • A white board or poster board to write questions on is helpful. University Communications and Marketing | American University 3 University Communications and Marketing | American University 4 Instagram Stories • Snapchat copy cat feature. • Disappear after 24 hours. • Featured at the top of everyone’s Instagram feed. • Edit photos inside Instagram app. University Communications and Marketing | American University 5 University Communications and Marketing | American University 6 Twitter Moments • Curate the best tweets from an event into a timeline users can swipe through. Source. University Communications and Marketing | American University 7 Frequent University Communications and Marketing | American University 8 LinkedIn Blog Posts • Leverage the expertise of faculty, featured speakers. University Communications and Marketing | American University 9 daily SNAPCHAT 100 active MILLION users of users log in 54% every day of users are 60% age 13-24 year-over-year increase in 400% daily video views (more than Facebook) University Communications and Marketing | American University 10 Snapchat Stories and Geofilters • Snapchat geofilters are graphic frames that overlay snaps. • You can purchase a geofilter through the Snapchat website. • Once you’ve created a geofilter, make a Snapchat story, so users can submit their snaps from your event for all to see.
    [Show full text]
  • “For Marketing to Succeed in the Digital Era, We Need to Stop Thinking
    avid Rogers, a faculty member at Columbia Business School, is a globally-recognized D leader on digital business strategy. He is author of four books, most recently The Digital Transformation Playbook (in 6 languages). Rogers delivers keynotes at conferences world- wide on the impact of digital technologies on business. David advises global companies such as Google, Merck, GE, Toyota, Cartier, Visa, China Eastern Airlines, and Pizza Hut on digital strategy and has led workshops for executives in hundreds of companies from 66 countries. “For marketing to succeed in the digital era, we need to stop thinking of customers www.davidrogers.biz as targets (to market at) and start seeing customers as networks (to connect with).” — David Rogers, faculty – Columbia Business School, and best-selling author, The Digital Transformation Playbook CHAPTER 1 Digital Marketing and the 4 P’s Digital Marketing has become mainstream. No longer is “digital” the parsley on the marketing plate at a fancy dinner, an afterthought, or a cool extra to add some sizzle to a marketing plan. Digital is now the main course of the marketing meal — the connective strategy that pulls all the elements of an integrated marketing program together, ensuring that all messaging and communications strategies are effective at reaching the customer. Yet in many organizations, the digital marketing mix is still handled arbitrarily, after the fact, or managed by a junior member of the team who has some personal familiarity or a willingness to learn how to work in the digital medium. This book is designed to bring the educated layperson — the typical marketer or business professional familiar with the topic — to a point where digital methods become an essential element of their integrated marketing strategy from the onset.
    [Show full text]