Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry.

In recent years, social media applications (e.g., Facebook, LinkedIn) have created mobile applications (apps) to give their members instant and real-time access from anywhere. To keep members informed and drive timely engagement, these mobile apps send event notifications. However, sending notifications for every possible event would result in too many notifications which would in turn annoy members and create a poor member experience.

Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.

Web search engines utilize behavioral signals to develop search experiences tailored to individual users. To be effective, such personalization relies on access to sufficient information about each user's interests and intentions. For new users or new queries, profile information may be sparse or non-existent. To handle these cases, and perhaps also improve personalization for those with profiles, search engines can employ signals from users who are similar along one or more dimensions, i.e., those in the same cohort. In this paper we describe a characterization and evaluation of the use of such cohort modeling to enhance search personalization. We experiment with three pre-defined cohorts-topic, location, and top-level domain preference-independently and in combination, and also evaluate methods to learn cohorts dynamically. We show via extensive experimentation with large-scale logs from a commercial search engine that leveraging cohort behavior can yield significant relevance gains when combined with a production search engine ranking algorithm that uses similar classes of personalization signal but at the individual searcher level. Additional experiments show that our gains can be extended when we dynamically learn cohorts and target easily-identifiable classes of ambiguous or unseen queries.