Social Referral : Leveraging network connections to deliver recommendations

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Short paper @ RecSys 2012 on Social Referral - A new paradigm for delivering recommendations

Short paper @ RecSys 2012 on Social Referral - A new paradigm for delivering recommendations

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  • 1. Social Referral: Leveraging network connections to deliver recommendations Mohammad Amin, Baoshi Yan, Sripad Sriram, Anmol Bhasin, Christian Posse Abstract ResultsOur contributions with this study are :1) A novel recommendation delivery paradigm called Social Referral, which utilizes a users social network for the delivery of relevant content.2) An implementation of the paradigm is deployed and evaluated in a real industrial production setting of a large online professional network.3) A study of the interaction between the trifecta of the recommender system, the trusted connections and the end consumer of the recommendation. Example: Social Referral for LinkedIn Group Recommendation Potential Members User is nudged to invite 1 2 Experiments  Emails sent to referrer: 85K  Direct Recomm Email (control grp) :16K User’s network  Percentage of Referral Sent by referrers: 7.8%  Join Rate - referral: 22.5%  Join Rate – direct recomm: 9.6% Identifying Groups to Refer • Stronger Connection Strength => Higher Acceptance  Group Quality:  Better Recommendation  Strongest Connection: ReleVance => Higher Acceptance  Combination: Summary  Stronger Connection Strength => Higher CTR We present Social Referral, a novel Email Experiment:  No significant change in recommendation delivery mechanism which Social Referral recommendation relevance for leverages a target users social network for the vs referrees after filtering by effective delivery of relevant recommendations referrers! to the user. To our knowledge, most previous Direct Recommendations work has been focused on incorporating social network to improve recommendation Discussion: relevance or explain recommendations. Little Social Referral has been explored on leveraging social vs network for the delivery of recommendations. Direct Recommendation We carry out a large scale user study with real  2 step loss for social referral vs one- world data that demonstrated the effectiveness step loss for direct recommendation. of social referral. Our study showed that referrers are receptive to passing along the VS.  Different user experience: Warm recommendation (social recommendations to their connections, and  recommendations delivered via social referrals referral) vs cold recommendation are more than twice as likely to be accepted (direct recommendation) by target users than those directly delivered to  For referrs, we could piggyback the them. social referral process at various engaging points for free