Social Correlation in Latent Spaces for Complex Networks

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Show Me the Money: Dynamic Recommendations for Revenue Maximization

Recommender Systems (RS) play a vital role in applications such as e-commerce and on-demand content streaming. Research on RS has mainly focused on the customer perspective, i.e., accurate prediction of user preferences and maximization of user utilities. As a result, most existing techniques are not explicitly built for revenue maximization, the primary business goal of enterprises. In this work, we explore and exploit a novel connection between RS and the profitability of a business. As recommendations can be seen as an information channel between a business and its customers, it is interesting and important to investigate how to make strategic dynamic recommendations leading to maximum possible revenue. To this end, we propose a novel \model that takes into account a variety of factors including prices, valuations, saturation effects, and competition amongst products. Under this model, we study the problem of finding revenue-maximizing recommendation strategies over a finite time horizon. We show that this problem is NP-hard, but approximation guarantees can be obtained for a slightly relaxed version, by establishing an elegant connection to matroid theory. Given the prohibitively high complexity of the approximation algorithm, we also design intelligent heuristics for the original problem. Finally, we conduct extensive experiments on two real and synthetic datasets and demonstrate the efficiency, scalability, and effectiveness our algorithms, and that they significantly outperform several intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity proo

Gamma-Poisson dynamic matrix factorization embedded with metadata influence

© 2018 Curran Associates Inc.All rights reserved. A conjugate Gamma-Poisson model for Dynamic Matrix Factorization incorporated with metadata influence (mGDMF for short) is proposed to effectively and efficiently model massive, sparse and dynamic data in recommendations. Modeling recommendation problems with a massive number of ratings and very sparse or even no ratings on some users/items in a dynamic setting is very demanding and poses critical challenges to well-studied matrix factorization models due to the large-scale, sparse and dynamic nature of the data. Our proposed mGDMF tackles these challenges by introducing three strategies: (1) constructing a stable Gamma-Markov chain model that smoothly drifts over time by combining both static and dynamic latent features of data; (2) incorporating the user/item metadata into the model to tackle sparse ratings; and (3) undertaking stochastic variational inference to efficiently handle massive data. mGDMF is conjugate, dynamic and scalable. Experiments show that mGDMF significantly (both effectively and efficiently) outperforms the state-of-the-art static and dynamic models on large, sparse and dynamic data

From purchase, usage, to upgrade — Consumer analytics using large scale transactional data

The amount of data businesses are collecting about their customers is staggering. Firms can now easily track and record past purchases, product usage patterns, and customers’ responses to marketing campaigns and promotion programs. If fully analyzed, such rich transaction data offers companies the opportunity to understand what drives customers’ purchase decisions, how to improve their shopping experience, and how to develop and retain loyal customers. My dissertation addresses these issues by applying consumer analytics, including association rule mining, survival analysis, econometrics, and optimization, on large-scale transactional data to help companies better understand, predict, and subsequently influence the consumption behavior of their customers. My dissertation comprises three essays. The first essay utilizes multi-level association rule mining to predict project-oriented purchases. In the second essay, I propose an Expo-Decay proportional hazard model and use customers’ adoptions and usage of previous product generations to predict their upgrade behaviors for the current product generation. In the third essay, a time-based dynamic synchronization policy is applied for the maintenance of consolidated data repository under an infinite planning horizon. In these essays, I apply and extend a variety of business analytics tools including data mining (association rule mining and collaborative filtering), survival analysis, dynamic programming, simulation, and econometric models. These essays contribute to the consumer analytics literature and can help firms maintain high-quality data assets and make informed decisions on cross-generation product development, product promotion and recommendation, and customer retention