Databases and Information Systems Commons^™

Expanding Controllability Of Hybrid Recommender Systems: From Positive To Negative Relevance, Behnam Rahdari, Chun-Hua Tsai, Peter Brusilovsky

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

A hybrid recommender system fuses multiple data sources, usually with static and nonadjustable weightings, to deliver recommendations. One limitation of this approach is the problem to match user preference in all situations. In this paper, we present two user-controllable hybrid recommender interfaces, which offer a set of sliders to dynamically tune the impact of different sources of relevance on the final ranking. Two user studies were performed to design and evaluate the proposed interfaces.

Studying And Handling Iterated Algorithmic Biases In Human And Machine Learning Interaction., Wenlong Sun

Electronic Theses and Dissertations

Algorithmic bias consists of biased predictions born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, the interaction between people and algorithms can exacerbate bias such that neither the human nor the algorithms receive unbiased data. Thus, algorithmic bias can be introduced not only before and after the machine learning process but sometimes also in the middle of the learning process. With a handful of exceptions, only a few categories of bias have been studied in Machine Learning, and there are few, if any, studies of the impact of bias on both human behavior and algorithm performance. …

Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …

Community Discovery In Heterogeneous Social Networks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch

Research Collection School Of Computing and Information Systems

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect. 3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees …

A Bystander's Dilemma: Participatory Design Study Of Privacy Expectations For Smart Home Devices, Oriana Mcdonough

Renée Crown University Honors Thesis Projects - All

Traditional homes have become increasingly filled with Internet-connected devices, turning them into “smart homes.” Currently, research around privacy concerns with smart home devices has focused on the end users. The goal for our research is to understand the perceptions and desired privacy mechanisms from the perspective of a different stakeholder, i.e., the bystanders. Bystanders in this context are individuals who are not the owner or primary user of smart home devices but are potentially affected by the device usage, such as house guests or family members. In order to understand this, we conducted a focus group study with co-design activities …

The Golden Ticket: How Blockchain Technology Can Be Implemented Into Event Ticketing, Jack Singer

Renée Crown University Honors Thesis Projects - All

When the group/individual named Satoshi Nakamoto first conceptualized blockchain in 2008, it served as the underlying foundation to the cryptocurrency Bitcoin. In the years following, cryptocurrencies alike experiences massive gains in profitability; however, after the bubble had burst organizations began to look at the technology from a more academic standpoint. It was quickly found out that there is a massive application for blockchain in almost all sectors of industry from bulk stores (Walmart) to banking (IBM). This paper will explore how blockchain technology can be implemented into event ticketing, more specifically concerts. The current landscape of the industry is under …

Exploring Data Science: Understanding, Predicting, & Visualizing Crime In Syracuse, Ryan French

Renée Crown University Honors Thesis Projects - All

With the advent of the open data portal for the city of Syracuse came an opportunity previously impossible; anyone could download, mine, and visualize information about Syracuse direct from the source. Over the course of this project, I will be performing these processes on a selection of crime data from 2017 in order to better understand the patterns of crime in Syracuse, where they occur, and if it can be predicted whether or not a crime will lead to an arrest.

This project will begin with an overview of the data, how it was obtained, and the meanings of the …

Data Gathering In Cognitive Radio Ad Hoc And Sensor Wireless Networks, Kimberly A. Brown

Theses and Dissertations

Data gathering is a network communication task in which all of the network’s nodes send their individual messages to a distinguished sink node. In cognitive radio ad hoc and sensor wireless networks (CR-AHSWNs), unlicensed secondary users (SUs) opportunistically use channels when the licensed primary users are not using them. Therefore, the channels available to each SU vary with time and location, which makes the development of data gathering algorithms for CR-AHSWNs challenging.

In this thesis, a data gathering protocol for CR-AHSWNs is proposed. The protocol consists of several distributed SU action selection and channel selection algorithms. An algorithm that can …

Querying Over Encrypted Databases In A Cloud Environment, Jake Douglas

Boise State University Theses and Dissertations

The adoption of cloud computing has created a huge shift in where data is processed and stored. Increasingly, organizations opt to store their data outside of their own network to gain the benefits offered by shared cloud resources. With these benefits also come risks; namely, another organization has access to all of the data. A malicious insider at the cloud services provider could steal any personal information contained on the cloud or could use the data for the cloud service provider's business advantage. By encrypting the data, some of these risks can be mitigated. Unfortunately, encrypting the data also means …

Multimodal Review Generation For Recommender Systems, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Key to recommender systems is learning user preferences, which are expressed through various modalities. In online reviews, for instance, this manifests in numerical rating, textual content, as well as visual images. In this work, we hypothesize that modelling these modalities jointly would result in a more holistic representation of a review towards more accurate recommendations. Therefore, we propose Multimodal Review Generation (MRG), a neural approach that simultaneously models a rating prediction component and a review text generation component. We hypothesize that the shared user and item representations would augment the rating prediction with richer information from review text, while sensitizing …

Project Sidewalk: A Web-Based Crowdsourcing Tool For Collecting Sidewalk Accessibility Data At Scale, Manaswi Saha, Michael Saugstad, Hanuma Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, Jon Froehlich

Research Collection School Of Computing and Information Systems

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement …

How To Derive Causal Insights For Digital Commerce In China? A Research Commentary On Computational Social Science Methods, David C.W. Phang, Kanliang Wang, Qiu-Hong Wang, Robert John Kauffman, Maurizio Naldi

Research Collection School Of Computing and Information Systems

The transformation of empirical research due to the arrival of big data analytics and data science, as well as the new availability of methods that emphasize causal inference, are moving forward at full speed. In this Research Commentary, we examine the extent to which this has the potential to influence how e-commerce research is conducted. China offers the ultimate in data-at-scale settings, and the construction of real-world natural experiments. Chinese e-commerce includes some of the largest firms involved in e-commerce, mobile commerce, social media and social networks. This article was written to encourage young faculty and doctoral students to engage …

Detect Rumors On Twitter By Promoting Information Campaigns With Generative Adversarial Learning, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumors can cause devastating consequences to individual and/or society. Analysis shows that widespread of rumors typically results from deliberately promoted information campaigns which aim to shape collective opinions on the concerned news events. In this paper, we attempt to fight such chaos with itself to make automatic rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, complicating the original conversational threads in order to pressurize the discriminator to learn stronger rumor indicative representations …

Robust Factorization Machine: A Doubly Capped Norms Minimization, Chenghao Liu, Teng Zhang, Jundong Li, Jianwen Yin, Peilin Zhao, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Factorization Machine (FM) is a general supervised learning framework for many AI applications due to its powerful capability of feature engineering. Despite being extensively studied, existing FM methods have several limitations in common. First of all, most existing FM methods often adopt the squared loss in the modeling process, which can be very sensitive when the data for learning contains noises and outliers. Second, some recent FM variants often explore the low-rank structure of the feature interactions matrix by relaxing the low-rank minimization problem as a trace norm minimization, which cannot always achieve a tight approximation to the original one. …

Beyond Autonomy: The Self And Life Of Social Agents, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Agents have gained popularity nowadays as virtual assistants and companions of their human users supporting daily activities in many aspects of personal life. Designed to be sociable, an agent engages its user(s) to communicate and even develop friendships. Rather than just as a lifeless toy, it is supposed to be perceived as an individual with its own personality, experiences, and social life. In this paper, we seek to highlight self-hood as another dimension that characterizes an agent. Besides levels of autonomy and reasoning, an agent can be defined based on its capacity to process and reflect on its own self …

Adaptive Resonance Theory (Art) For Social Media Analytics, Lei Meng, Ah-Hwee Tan, Donald C. Ii Wunsch

Research Collection School Of Computing and Information Systems

The last decade has witnessed how social media in the era of Web 2.0 reshapes the way people communicate, interact, and entertain in daily life and incubates the prosperity of various user-centric platforms, such as social networking, question answering, massive open online courses (MOOC), and e-commerce platforms. The available rich user-generated multimedia data on the web has evolved traditional ways of understanding multimedia research and has led to numerous emerging topics on human-centric analytics and services, such as user profiling, social network mining, crowd behavior analysis, and personalized recommendation. Clustering, as an important tool for mining information groups and in-group …

Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …

Cure: Flexible Categorical Data Representation By Hierarchical Coupling Learning, Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, Hang Gao

Research Collection School Of Computing and Information Systems

The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into …

Unifying Knowledge Graph Learning And Recommendation: Towards A Better Understanding Of User Preferences, Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system. In this paper, we jointly learn the model of recommendation …

Learning Two-Layer Neural Networks With Symmetric Inputs, Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

Research Collection School Of Computing and Information Systems

We give a new algorithm for learning a two-layer neural network under a very general class of input distributions. Assuming there is a ground-truth two-layer network $y = A \sigma(Wx) + \xi$, where A, W are weight matrices, $\xi$ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters A, W of the ground-truth network. The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework …