Optimization of Item Selection with Prediction Uncertainty

Selecting items from a candidate pool to maximize the total return is a classical problem, which is faced by people frequently in real life and also engineers in information technology industry, e.g., digital advertising, e-commerce, web search, etc. For example, web UI designers always try to find the best web design among many candidates to display to users, Google needs to select personalized engaging ads to display to users based on their historical online behaviors. Each of these industries has hundreds of billions of dollars market, which means that even a small performance improvement of item selection efficiency can drive hundreds of millions of dollars growth in the real world. In these applications, the true value of each item is unknown and can only be estimated from observed historical data. There is a large volume of significant research about building prediction models which are trained on historical data to estimate the item values. Given data volume and computation resource restrictions, engineers choose different models, e.g., deep neutral network, gradient boosting tree, or logistic regression to solve the problems. We will not dive into this area too much in this dissertation. Instead, our focus is how to maximize the total return given these predictions, especially taking into account the prediction uncertainties for the value optimization.In the large-scale real applications, the candidate pool can be extraordinary large. It is infeasible to pick some items from the pool to get interactive feedback for exploration. Actually, not only is exploration infeasible, but even estimating the value of each item through a complex estimation mode is almost impossible due to the need of real-time response. For example, Apple needs to estimate users’ favorite apps and recommends them to users when they visit Apple store. Google needs to select ads to display to users given a users’ search queries. There are millions of candidates needing to be estimated from prediction models. It is very challenging to support such a large scale of model prediction under the low-latency constraint. Besides that, to have a good prediction accuracy, the models used in industry are getting more and more complex, e.g hidden neurons and layers of deep neural network increases rapidly in real applications, which also increases latency significantly. All of these make it infeasible to evaluate all candidates through one single complex model in large scale application. To solve this problem, engineers usually leverage the cascading waterfall filtering method to filter items sequentially, which means instead of using one complex model to estimate the values of all candidates, multiple stages are adopted to filter out candidates sequentially. For example, a simple model is used in the first stage to estimate candidates’ values for choosing a small subset from all candidates. These selected items are then passed to another stage to be estimated by a more complex model. Intuitively, this cascading waterfall filtering method provides a good trade-off between infrastructure cost and prediction accuracy, which can save computational resources use substantially, and simultaneously select most promising items accurately. However, there is no systematic study about how to efficiently choose the number of waterfalls and how many filtered items in each waterfall. Engineers tune the settings of this system heuristically through personal experience or online experiments, which is very inefficient, especially when the system is dynamic and changes rapidly. In this dissertation, we propose a theoretical framework for the cascading waterfall filtering problem and develop a mathematical algorithm to obtain the optimal solutions. Our method achieves a dramatic improvement in an important real-world application, which adopts cascading water filtering system to select a few items from tens of millions of candidates.There are also some cases in which the candidate pool is relatively small. For instance, the number of web UI candidates is usually less than one hundred. Then, we are able to explore during item selection process. A typical exploration case is online experimentation, which is widely used to test and select items in real applications. In this situation, we can get interactive feedback to evaluate items. Considering online experiments for example, we usually randomly segments users into several groups, show them different candidates, and then compare the overall performance of each candidate to find the item with the largest value. Among all designs, A/B testing, which usually segments users into two statasitically equivalent groups to measure the difference between two versions of a single variable, is the most popular. For instance, in order to compare the impact of an ad versus another, we need to see the impact of exposing a user to viewing the first ad, and not the second, and then compare with the converse situation. However, a user cannot both see the first ad and not see it. Consequently, we need to create two “statistically equivalent populations” and expose users randomly to one or the other. This method is straightforward. However, the defect of this method is also obvious: to measure both versions, this method cannot expose all users to the best version, which leads to potential value loss. Some multi-armed bandit algorithms, e.g., Randomized Probability Matching (RPM), Upper Confidence Bounds (UCB), whose objective is maximizing the total return in experiment, have been proposed for improvement. However, these methods do not take into account the statistical confidence levels of the final result from the experiment and the corresponding impact on the subsequent item selection in the post-experimental stage. To solve this problem, we develop algorithms to achieve a good trade-off between reducing statistical uncertainty and maximizing cumulative reward, which aims at maximizing the total expected reward of item selection over a total duration, which includes both the current experimental stage and the post-experimental stage. The proposed algorithms demonstrate consistent and statistically significant improvements across different settings, outperforming both A/B testing and multi-armed bandit algorithms significantly

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence