Leveraging aggregate ratings for improving predictive performance of recommender systems

This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of recommender systems, including two types of collaborating filtering and a hierarchical linear regression model. First, we present a framework for incorporating aggregate rating information and apply this framework to the aforementioned individual rating models. Then we formally show that this additional aggregate rating information provides more accurate recommendations of individual items to individual users. Further, we experimentally confirm this theoretical finding by demonstrating on several datasets that the aggregate rating information indeed leads to better predictions of unknown ratings. We also propose scalable methods for incorporating this aggregate information and test our approaches on large datasets. Finally, we demonstrate that the aggregate rating information can also be used as a solution to the cold start problem of recommender systems.NYU, Stern School of Business, Center for Digital Economy Researc

Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions

Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions

The Gestalt in Graphs: Prediction Using Economic Networks

We define an economic network as a linked set of entities, where links are created by actual realizations of shared economic outcomes between entities. Such networks are becoming increasingly prevalent on the Internet, an example being the copurchase netwok on Amazon where entities are books and links designate which pairs were purchased simultaneously. Our dataset covers a diverse set of books spanning over 400 categories over a period of three years with a total of over 70 million observations. To our knowledge, this is the first large scale study showing that an economic network contains useful predictive information that is distributed in the network. We show that an economic network contains predictive information. Specifically, we demonstrate that an entity’s future demand is more accurately predicted by combining its historical demand with that of its neighbors than by considering its demand alone. In other words, if you want to know what your state will be in the future, consider what is happening to your neighbors now. This result could apply to other economic networks where outcomes of sets of entities tend to be related.NYU Stern School of Busines

ABSTRACT Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions

Improving Collaborative Filtering Recommendations Using External Data

Abstrac

Monetizing Freemium Communities: Does Paying for Premium Increase Social Engagement?

Making sustainable profits from a baseline zero price and motivating free consumers to convert to premium subscribers is a continuing challenge for all freemium communities. Prior research has causally established that social engagement (Oestreicher-Singer and Zalmanson 2013) and peer influence (Bapna and Umyarov 2015) are two important drivers of users converting to premium subscribers in such communities. In this paper, we flip the perspective of prior research and ask whether the decision to pay for a premium subscription causes users to become more socially engaged. In the context of the Last.fm music listening freemium social community, we establish, using a novel 41-month-long panel dataset, a look-ahead propensity score matching (LA-PSM) procedure coupled with a difference-in-difference estimator of the treatment effect, that payment for premium leads to more social engagement. Specifically, we find that paying for premium leads to an increase in both content-related and community-related social engagement. Free users who convert to premium listen to 287.2% more songs, create 1.92% more playlists, exhibit a 2.01% increase in the number of forum posts made, and gain 15.77% more friends. Thus, premium subscribers create value not only for themselves by consuming more content, but also for the community and site by organizing more content and adding more friends, who are subsequently engaged by the social diffusion emerging from the focal user’s activities