Chu-Cheng Hsieh

Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of… Show more

Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of training instances. As a consequence, the lack of training data prevents ASR systems from accurately learning users acoustic biometrics, jeopardizes the downstream applications, and eventually impairs user experience.

In this work, we propose an adversarial few-shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based few-shot learning to learn speaker acoustic representations, where the limited instances are comprehensively utilized to improve the identification performance. In addition, adversarial learning is applied to further enhance the generalization and robustness for speaker identification with adversarial examples. Experiments conducted on a publicly available large-scale dataset demonstrate that \model significantly outperforms eleven baseline methods. An in-depth analysis further indicates both effectiveness and robustness of the proposed method. Show less

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising… Show more

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer’s issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrates how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named Isa (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications. Show less

The effectiveness of monetary promotions has been well reported in the literature to affect shopping decisions for utilitarian products in real life experience [3]. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that online consumers can easily hunt for the best products with the highest value at a reasonable price. We study e-commerce data – shopping
receipts collected from email accounts, and conclude that for utilitarian products like books or… Show more

The effectiveness of monetary promotions has been well reported in the literature to affect shopping decisions for utilitarian products in real life experience [3]. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that online consumers can easily hunt for the best products with the highest value at a reasonable price. We study e-commerce data – shopping
receipts collected from email accounts, and conclude that for utilitarian products like books or electronics, buyers are price sensitive and are willing to delay the purchase for better deals. We then present a real-time promotion framework, called the RTP framework: a one-time promoted discount price is offered to allure a potential buyer making a decision promptly. To achieve more ef-
fectiveness on real-time promotion in pursuit of better profits, we propose two discount-giving strategies: an algorithm based on Kernel density estimation, and the other algorithm based on Thompson sampling strategy. We show that, given a pre-determined discount budget, our algorithms can significantly acquire better revenue in return than classical strategies with simply fixed discount on label price, demonstrating its feasibility to be a promising deployment in e-commerce services for real-time promotion. Show less

Query suggestions have been a valuable feature for e-commerce sites in helping shoppers refine their search intent. In this paper, we develop an algorithm that helps e-commerce sites like eBay mingle the output of different recommendation algorithms. Our algorithm is based on “Thompson Sampling” — a technique designed for solving multi-arm bandit problems where the best results are not known in advance but instead are tried out to gather feedback. Our approach is to treat query suggestions as a… Show more

Query suggestions have been a valuable feature for e-commerce sites in helping shoppers refine their search intent. In this paper, we develop an algorithm that helps e-commerce sites like eBay mingle the output of different recommendation algorithms. Our algorithm is based on “Thompson Sampling” — a technique designed for solving multi-arm bandit problems where the best results are not known in advance but instead are tried out to gather feedback. Our approach is to treat query suggestions as a competition among data resources: we have many query suggestion candidates competing for limited space on the search results page. An “arm” is played when a query suggestion candidate is chosen for display, and our goal is to maximize the expected reward (user clicks on a suggestion). Our experiments have shown promising results in using the click-based user feedback to drive success by enhancing the quality of query suggestions. Show less

During the shopping process, users typically narrow down their search to a small collection of products before making a final purchase. These data, consisting of products that users are considering purchasing, correlate strongly with user search intent and product desirability. By allowing users to bookmark products between browsing and purchasing, we collect user-interest information. We then propose a product recommendation algorithm based on these data. By considering both popular and… Show more

During the shopping process, users typically narrow down their search to a small collection of products before making a final purchase. These data, consisting of products that users are considering purchasing, correlate strongly with user search intent and product desirability. By allowing users to bookmark products between browsing and purchasing, we collect user-interest information. We then propose a product recommendation algorithm based on these data. By considering both popular and long-tail queries, we shed light on the potential usage of the data. Show less

In this paper, we propose topic models to deal with social network data. Our topic models are specialized in dealing with \popularity bias" caused by dominance of a limited number of popular user (or node) in a dataset. These popular nodes have been simply removed in topic models because they do not have much meaning (e.g., the and is). However, in a social network dataset, most people are interested in popular users (e.g., Barack Obama and Britney Spears) and they should be carefully… Show more

In this paper, we propose topic models to deal with social network data. Our topic models are specialized in dealing with \popularity bias" caused by dominance of a limited number of popular user (or node) in a dataset. These popular nodes have been simply removed in topic models because they do not have much meaning (e.g., the and is). However, in a social network dataset, most people are interested in popular users (e.g., Barack Obama and Britney Spears) and they should be carefully handled.

To solve this problem, we introduce a notion of "popularity component" and explore various ways to e?ectively incorporate it. Through extensive experiments, we show that our proposed models achieve signi?cant improvements over the existing models in terms of lowering "perplexity". We
also show that the outgoing edge degree (how many people a user follows) does not help much in achieving the lower perplexity. Our models can be useful in providing more accurate recommendations and clusterings for various services including social network services. Show less

Recently social collaboration projects such as Wikipedia and Flickr have been gaining popularity, and more and more social tag information is being accumulated. In this study, we demonstrate how to effectively use social tags created by humans to find similar items. We create a query-by-example interface for finding similar items through offering examples as a query. Our work aims to measure the similarity between a query, expressed as a group of items, and another item through utilizing the… Show more

Recently social collaboration projects such as Wikipedia and Flickr have been gaining popularity, and more and more social tag information is being accumulated. In this study, we demonstrate how to effectively use social tags created by humans to find similar items. We create a query-by-example interface for finding similar items through offering examples as a query. Our work aims to measure the similarity between a query, expressed as a group of items, and another item through utilizing the tag information. We show that using human-generated tags to find similar items has at least two major challenges: popularity bias and the missing tag effect. We propose several approaches to overcome the challenges. We build a prototype website allowing users to search over all entries in Wikipedia based on tag information, and then collect 600 valid questionnaires from 69 students to create a benchmark for evaluating our algorithms based on user satisfaction. Our results show that the presented techniques are promising and surpass the leading commercial product, Google Sets, in terms of user satisfaction. Show less

Even though numerous kinds of anti-virus software packages have been used for many years, previously unseen malware is still a serious threat to computer and information system. By analyzing portable executable header entries of executables, a malware detection model which consists of four stages: attribute extraction, attribute binarization, attribute elimination, and feature selection and classifier training was carried out in this study. First, we collected header entries from all… Show more

Even though numerous kinds of anti-virus software packages have been used for many years, previously unseen malware is still a serious threat to computer and information system. By analyzing portable executable header entries of executables, a malware detection model which consists of four stages: attribute extraction, attribute binarization, attribute elimination, and feature selection and classifier training was carried out in this study. First, we collected header entries from all executables in our dataset and viewed each entry as a potential attribute. Second, information gain and gain ratio were used to binarize numerical and nominal attributes. Next, useless and redundant attributes were eliminated in the third stage. Finally, by using support vector machine which is a classification algorithm of conspicuous generalization ability, feature selection was simultaneously performed with classifier training to reduce the number of attributes and retain the performance of classifier in a cost-effective. We evaluated our model by 1,908 benign programs and 7,863 malicious files (virus, email worm, trojan and backdoor) and estimated its generalization ability by cross validation. The experiment results showed that our model had promising performance for detecting virus and email worm. Show less

Owing to the lack of prevention ability of traditional anti-virus methods, a behavior-based virus prevention model for detecting unknown virus is proposed in this study. We first defined the behaviors of an executable by observing its usage of dynamically linked libraries and Application Programming Interfaces. Then, information gain and support vector machines were applied to filter out the redundant behavior attributes and select informative feature for training a virus classifier. The… Show more

Owing to the lack of prevention ability of traditional anti-virus methods, a behavior-based virus prevention model for detecting unknown virus is proposed in this study. We first defined the behaviors of an executable by observing its usage of dynamically linked libraries and Application Programming Interfaces. Then, information gain and support vector machines were applied to filter out the redundant behavior attributes and select informative feature for training a virus classifier. The performance of our model was evaluated by a dataset contains 1,758 benign executables and 846 viruses. The experiment results are promising, and the overall accuracies are 99% and 96.66% for detecting the known viruses and the previously unseen viruses respectively. Show less

In the finance domain, the famous Efficient Market Hypothesis(EMH) concludes that crowd wisdom is superior to any expert wisdom in picking financial stocks. In this study, we test a similar hypothesis in the domain of news recommendation by conducting experiments on Twitter. We first identify a group of experts on Twitter who have consistently identified ``interesting'' (or popular) news early on and have recommended them in their tweets. We then collect two sets of news: a set of incoming news… Show more

In the finance domain, the famous Efficient Market Hypothesis(EMH) concludes that crowd wisdom is superior to any expert wisdom in picking financial stocks. In this study, we test a similar hypothesis in the domain of news recommendation by conducting experiments on Twitter. We first identify a group of experts on Twitter who have consistently identified ``interesting'' (or popular) news early on and have recommended them in their tweets. We then collect two sets of news: a set of incoming news recommended by these experts and a similar set recommended by the ``crowd''. We then observe, for a few months, how widely the news in the two sets are circulated on Twitter, and evaluate which set contains more widely-circulated news (and therefore are more likely to be interesting).After conducting repeated experiments, we draw a similar conclusion to the EMH -- the crowd wisdom is always the winner in our experiments; we could not identify an expert group whose news recommendation performance was consistently better than that of the crowd. We then proceed to investigate whether the expert wisdom can be used to improve crowd wisdom in any way. Show less