Rupesh Gupta

Activity ranking in LinkedIn feed

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a users network neighborhood forms a stream of updates for the users consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.

Email Volume Optimization at LinkedIn

Online social networking services distribute various types of messages to their members. Common types of messages include news, connection requests, membership notifications, promotions and event notifications. Such communication, if used judiciously, can provide an enormous value to members thereby keeping them engaged. However sending a message for every instance of news, connection request, or the like can result in an overwhelming number of messages in a members mailbox. This may result in reduced effectiveness of communication if the messages are not sufficiently relevant to the members interests. It may also result in a poor brand perception of the networking service. In this paper we discuss our strategy and experience with regard to the problem of email volume optimization at LinkedIn. In particular, we present a cost-benefit analysis of sending emails, the key factors to administer an effective volume optimization, our algorithm for volume optimization, the architecture of the supporting system and experimental results from online A/B tests.

Optimizing Email Volume For Sitewide Engagement

In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service. Email volume optimization approaches published in the past have proposed optimization of email volume for maximization of engagement metrics which are impacted exclusively by email; for example, the number of sessions that begin with clicks on links within emails. The impact of email on such downstream engagement metrics can be estimated easily because of the ease of attribution of such an engagement event to an email. However, this framework is limited in its view of the ecosystem of the networking service which comprises of several tools and utilities that contribute towards delivering value to members; with email being just one such utility. Thus, in this paper we depart from previous approaches by exploring and optimizing the contribution of email to this ecosystem. In particular, we present and contrast the differential impact of email on sitewide engagement metrics for various types of users. We propose a new email volume optimization approach which maximizes sitewide engagement metrics, such as the total number of active users. This is in sharp contrast to the previous approaches whose objective has been maximization of downstream engagement metrics. We present details of our prediction function for predicting the impact of emails on a users activeness on the mobile or web application. We describe how certain approximations to this prediction function can be made for solving the volume optimization problem, and present results from online A/B tests.