Author name cluster

Prem Gopalan

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5 papers

2 author rows

UAI Conference 2015 Conference Paper

Scalable Recommendation with Hierarchical Poisson Factorization

Prem Gopalan
Jake M. Hofman
David M. Blei

We develop hierarchical Poisson matrix factorization (HPF), a novel method for providing users with high quality recommendations based on implicit feedback, such as views, clicks, or purchases. In contrast to existing recommendation models, HPF has a number of desirable properties. First, we show that HPF more accurately captures the long-tailed user activity found in most consumption data by explicitly considering the fact that users have finite attention budgets. This leads to better estimates of users’ latent preferences, and therefore superior recommendations, compared to competing methods. Second, HPF learns these latent factors by only explicitly considering positive examples, eliminating the often costly step of generating artificial negative examples when fitting to implicit data. Third, HPF is more than just one method— it is the simplest in a class of probabilistic models with these properties, and can easily be extended to include more complex structure and assumptions. We develop a variational algorithm for approximate posterior inference for HPF that scales up to large data sets, and we demonstrate its performance on a wide variety of real-world recommendation problems—users rating movies, listening to songs, reading scientific papers, and reading news articles.

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NeurIPS Conference 2014 Conference Paper

Content-based recommendations with Poisson factorization

Prem Gopalan
Laurent Charlin
David Blei

We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences. CTPF can be used to build recommender systems by learning from reader histories and content to recommend personalized articles of interest. In detail, CTPF models both reader behavior and article texts with Poisson distributions, connecting the latent topics that represent the texts with the latent preferences that represent the readers. This provides better recommendations than competing methods and gives an interpretable latent space for understanding patterns of readership. Further, we exploit stochastic variational inference to model massive real-world datasets. For example, we can fit CPTF to the full arXiv usage dataset, which contains over 43 million ratings and 42 million word counts, within a day. We demonstrate empirically that our model outperforms several baselines, including the previous state-of-the-art approach.

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NeurIPS Conference 2013 Conference Paper

Efficient Online Inference for Bayesian Nonparametric Relational Models

Dae Il Kim
Prem Gopalan
David Blei
Erik Sudderth

Stochastic block models characterize observed network relationships via latent community memberships. In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size. We introduce a new model for these phenomena, the hierarchical Dirichlet process relational model, which allows nodes to have mixed membership in an unbounded set of communities. To allow scalable learning, we derive an online stochastic variational inference algorithm. Focusing on assortative models of undirected networks, we also propose an efficient structured mean field variational bound, and online methods for automatically pruning unused communities. Compared to state-of-the-art online learning methods for parametric relational models, we show significantly improved perplexity and link prediction accuracy for sparse networks with tens of thousands of nodes. We also showcase an analysis of LittleSis, a large network of who-knows-who at the heights of business and government.

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NeurIPS Conference 2013 Conference Paper

Modeling Overlapping Communities with Node Popularities

Prem Gopalan
Chong Wang
David Blei

We develop a probabilistic approach for accurate network modeling using node popularities within the framework of the mixed-membership stochastic blockmodel (MMSB). Our model integrates some of the basic properties of nodes in social networks: homophily and preferential connection to popular nodes. We develop a scalable algorithm for posterior inference, based on a novel nonconjugate variant of stochastic variational inference. We evaluate the link prediction accuracy of our algorithm on eight real-world networks with up to 60, 000 nodes, and 24 benchmark networks. We demonstrate that our algorithm predicts better than the MMSB. Further, using benchmark networks we show that node popularities are essential to achieving high accuracy in the presence of skewed degree distribution and noisy links---both characteristics of real networks.

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NeurIPS Conference 2012 Conference Paper

Scalable Inference of Overlapping Communities

Prem Gopalan
Sean Gerrish
Michael Freedman
David Blei
David Mimno

We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel. It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60, 000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms.

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