The Amazon video homepage is the primary gateway for customers looking to explore the large collection of content, and finding something interesting to watch. Typically, the page is personalized for a customer, and consists of a series of widgets or carousels, with each widget containing multiple items (e.g., movies, TV shows etc). Ranking the widgets needs to maximize relevance, and maintain diversity, while simultaneously satisfying business constraints. We present the first unified framework for dealing with relevance, diversity, and business constraints simultaneously. Towards this end, we derive a novel primal-dual algorithm which incorporates local diversity constraints as well as global business constraints for whole page optimization. Through extensive offline experiments and an online A/B test, we show that our proposed method achieves significantly higher user engagement compared to existing methods, while also simultaneously satisfying business constraints. For instance, in an online A/B test, our framework improved key metrics such as customer streaming minutes by 0.77% and customer distinct streaming days by 0.32% over a state-of-the-art submodular diversity model.

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting alignment problem. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.

Online reviews have become an inevitable part of a consumer's decision making process, where the likelihood of purchase not only depends on the product's overall rating, but also on the description of its aspects. Therefore, e-commerce websites such as Amazon and Walmart constantly encourage users to write good quality re- views and categorically summarize different facets of the products. However, despite such attempts, it takes a significant effort to skim through thousands of reviews and look for answers that address the query of consumers. For example, a gamer might be interested in buying a monitor with fast refresh rates and support for Gsync and Freesync technologies, while a photographer might be interested in aspects such as color depth and accuracy. To address these chal- lenges, in this paper, we propose a generative aspect summarization model called APSUM that is capable of providing fine-grained sum- maries of online reviews. To overcome the inherent problem of aspect sparsity, we impose dual constraints: (a) a spike-and-slab prior over the document-topic distribution and (b) a linguistic su- pervision over the word-topic distribution. Using a rigorous set of experiments, we show that the proposed model is capable of out- performing the state-of-the-art aspect summarization model over a variety of datasets and deliver intuitive fine-grained summaries that could simplify the purchase decisions of consumers.

Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate consistent improvement of the proposed approach over the current state-of-the-arts. Specifically, our approach empirically attains the information theoretic limits under the benchmark Stochastic Block Models and exhibits better stability and accuracy over the best known algorithms in the community recovery limits.

Easy accessibility can often lead to over-consumption, as seen in food and alcohol habits. On video on-demand (VOD) services, this has recently been referred to as binge watching, where potentially entire seasons of TV shows are consumed in a single viewing session. While a user viewership model may reveal this binging behavior, creating an accurate model has several challenges, including censored data, deviations in the population, and the need to consider external influences on consumption habits. In this paper, we introduce a novel statistical mixture model that incorporates these factors and presents a first of its kind characterization of viewer consumption behavior using a real-world dataset that includes playback data from a VOD service. From our modeling, we tackle various predictive tasks to infer the consumption decisions of a user in a viewing session, including estimating the number of episodes they watch and classifying if they continue watching another episode. Using these insights, we then identify binge watching sessions based on deviation from normal viewing behavior. We observe different types of binging behavior, that binge watchers often view certain content out-of-order, and that binge watching is not a consistent behavior among our users. These insights and our findings have application in VOD revenue generation, consumer health applications, and customer retention analysis.

We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural way. We show how the estimation of latent rankings in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable consistency as well as sample and computational complexity guarantees. We demonstrate that the new approach is empirically competitive with the current state-of-the-art approaches in predicting preferences on some semi-synthetic and real world datasets.

We propose a novel model for rank aggregation from pairwise comparisons which accounts for a heterogeneous population of inconsistent users whose preferences are different mixtures of multiple shared ranking schemes. By connecting this problem to recent advances in the non-negative matrix factorization (NMF) literature, we develop an algorithm that can learn the underlying shared rankings with provable statistical and computational efficiency guarantees. We validate the approach using semi-synthetic and real world datasets.

Topic modeling for large-scale distributed web-collections requires distributed techniques that account for both computational and communication costs. We consider topic modeling under the separability assumption and develop novel computationally efficient methods that provably achieve the statistical performance of the state-of-the-art centralized approaches while requiring insignificant communication between the distributed document collections. We achieve tradeoffs between communication and computation without actually transmitting the documents. Our scheme is based on exploiting the geometry of normalized word-word co-occurrence matrix and viewing each row of this matrix as a vector in a high-dimensional space. We relate the solid angle subtended by extreme points of the convex hull of these vectors to topic identities and construct distributed schemes to identify topics.

We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available. We show that the Bayes-optimum decision boundary is a hyperplane under a mapping defined by the likelihood function. Combining this with the maximum margin principle yields kernels for SVMs that leverage knowledge of the sensing model in an optimal way. We derive the optimum kernel for the bag-of-words (BoWs) sensing model and demonstrate its superior performance over other kernels in document and image classification tasks. These results indicate that such optimum sensing-aware kernel SVMs can match the performance of rather sophisticated state-of-the-art approaches.

A new geometrically-motivated algorithm for topic modeling is developed and applied to the discovery of latent “topics” in text and image “document” corpora. The algorithm is based on robustly finding and clustering extreme-points of empirical cross-document word-frequencies that correspond to novel words unique to each topic. In contrast to related approaches that are based on solving non-convex optimization problems using suboptimal approximations, locally-optimal methods, or heuristics, the new algorithm is convex, has polynomial complexity, and has competitive qualitative and quantitative performance compared to the current state- of-the-art approaches on synthetic and real-world datasets.

We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms with provable guarantees based on data-dependent and random projections to identify novel words and associated topics. Our key insight here is that the maximum and minimum values of cross-document frequency patterns projected along any direction are associated with novel words. While our sample complexity bounds for topic recovery are similar to the state-of-art, the computational complexity of our random projection scheme scales linearly with the number of documents and the number of words per document. We present several experiments on synthetic and realworld datasets to demonstrate qualitative and quantitative merits of our scheme.