Increasing engagement through early recommender intervention

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Increasing engagement through early recommender intervention

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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Applications table of contents

Pages 85-92

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Jill Freyne	CLARITY Center for Sensor Web Technologies, Dublin, Ireland
Michal Jacovi	IBM Research, Haifa, Israel
Ido Guy	IBM Research, Haifa, Israel
Werner Geyer	IBM Research, Boston, USA

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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ABSTRACT

Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Bonhard, P. and Sasse, M. A. 'Knowing me, knowing you' -- Using Profiles and Social Networking to Improve Recommender Systems. BT Technology Journal 24, 3(2006), 84--98.
	2	Burke, M., Marlow, C., and Lento, T. Feed Me: Motivating Newcomer Contribution in Social Network Sites. Proc. CHI'09, (2009).
	3	Chen, J., Geyer W., Dugan C., Muller, M., Guy I. Make New Friends, but Keep the Old -- Recommending People on Social Networking Sites. Proc. CHI'09, (2009).
	4	comScore, Inc. Retrieved from http://www.comscore.com/press/release.asp?press=2698 on 29/01/09.
	5	DiMicco, J.M., Millen, D.R., Geyer, W., Dugan, C., Brownholtz, B. and Muller, M. Motivations for Social Networking at Work. Proc. CSCW'08 (2008), 711--720.
	6	Drenner S., Sen S., and Tarveen L. Crafting the Initial User Experience to Achieve Community Goals. Proc. RecSys'08, (2008), 187--194.
	7	Dugan, C., Geyer, W., Muller, M., DiMicco, J.M., Brownholtz, B. and Millen, D.R. It's All 'About You' -- Diversity in Online Profiles. Proc. CSCW'08, (2008), 703--706.
	8	Farzan, R., DiMicco, J., Millen, D. R., Dugan, C., Geyer, W. and Brownholtz, E. Results from deploying a participation incentive mechanism within the enterprise. Proc. CHI'08, (2008), 563--572.
	9	Facebook Statistics, 2008. Retrieved from http://www.facebook.com/press/info.php?statistics on 29/01/09.
	10	Geyer, W., Dugan, C., Millen, D.R., Muller, M., Freyne, J. Recommending Topics for Self-Descriptions in Online User Profiles. Proc. RecSys'08, (2008), 59--66.
	11	Groh, G., and Ehmig, C. Recommendations in Taste Related Domains: Collaborative Filtering vs. Social Filtering. Proc. Group'07, (2007), 127--136.
	12	Guy, I., Jacovi, M., Shahar, E., Meshulam, N., Soroka, V. and Farrell,S. Harvesting with SONAR: the value of aggregating social network information. Proc. CHI'08 (2008), 1017--1026.
	13	Guy I., Ronen I., Wilcox E. Do You Know? Recommending People to Invite into Your Social Network. Proc. IUI'09, (2009).
	14	Harper, F.M., Frankowski, D., Drenner, S., Ren, Y.,Kiesler, S.Terveen, L.,Kraut, R., and Riedl, J. Talk Amongst Yourselves: Inviting Users to Participate in Online Conversations. Proc. IUI'07, (2007), 62--71.
	15	Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. An algorithmic framework for performing collaborative filtering. Proc. SIGIR'99, (1999), 230--237.
	16	Kohrs, A., and Merialdo, B. Improving Collaborative Filtering for New Users by Smart Object Selection. Proc. ICMF 2001
	17	Markus, M. L. Toward a Critical Mass Theory of Interactive Media: Universal Access, Interdependence, and Diffusion, Communications Research, 14(5), 1987, 491--511
	18	Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., and Riedl. J. Getting to Know You: Learning New User Preferences in Recommender Systems. Proc. IUI'02, (2002), 127--134
	19	Sinha, R. and Swearingen, K. Comparing Recommendations made by Online Systems and Friends. Proc. DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, (2001).
	20	Smith, M.A. and Kollock, P., "Communities in Cyberspace" (199) In Communities in Cyberspace, Routledge, London, 1999.
	21	Spertus, E., Sahami, M., and Buyukkokten, O. Evaluating similarity measures: a large-scale study in the Orkut social network. Proc. SIGKDD'05. (2005), 678--684.
	22	Stecher, K., Counts, S. Thin Slices of Online Profile Attributes. Proc. ICWSM'08 (2008).
	23	Sun, L. and Vassileva, J. Social Visualization Encouraging Participation in Online Communities. Groupware: Design, Implementation, and Use, 349--363, 2006