Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.Comment: 12 pages, 6 figure

Statistical learning methods for mining marketing and biological data

Nowadays, the value of data has been broadly recognized and emphasized. More and more decisions are made based on data and analysis rather than solely on experience and intuition. With the fast development of networking, data storage, and data collection capacity, data have increased dramatically in industry, science and engineering domains, which brings both great opportunities and challenges. To take advantage of the data flood, new computational methods are in demand to process, analyze and understand these datasets. This dissertation focuses on the development of statistical learning methods for online advertising and bioinformatics to model real world data with temporal or spatial changes. First, a collaborated online change-point detection method is proposed to identify the change-points in sparse time series. It leverages the signals from the auxiliary time series such as engagement metrics to compensate the sparse revenue data and improve detection efficiency and accuracy through smart collaboration. Second, a task-specific multi-task learning algorithm is developed to model the ever-changing video viewing behaviors. With the 1-regularized task-specific features and jointly estimated shared features, it allows different models to seek common ground while reserving differences. Third, an empirical Bayes method is proposed to identify 3\u27 and 5\u27 alternative splicing in RNA-seq data. It formulates alternative 3\u27 and 5\u27 splicing site selection as a change-point problem and provides for the first time a systematic framework to pool information across genes and integrate various information when available, in particular the useful junction read information, in order to obtain better performance

Social-Context Middleware for At-Risk Veterans

Many veterans undergo challenges when reintegrating into civilian society. These challenges include readapting to their communities and families. During the reintegration process veterans have difficulties finding employment, education or resources that aid veteran health. Research suggests that these challenges often result in veterans encountering serious mental illness. Post-Traumatic Stress Disorder (PTSD) is a common mental disease that veterans often develop. This disease impacts between 15-20% of veterans. PTSD increases the likelihood of veterans engaging in high risk behaviors which may consist of impulsivity, substance abuse, and angry outbursts. These behaviors raise the veterans’ risk of becoming violent and lashing out at others around them. In more recent studies the VA has started to define PTSD by its association to specific high risk behaviors rather than defining PTSD based on a combination of psychiatric symptoms. Some researchers have suggested that high risk behaviors -- extreme anger (i.e., rage or angry outbursts) is particularly problematic within the context of military PTSD. Comparatively little research has been done linking sensor based systems to identify these angry episodes in the daily lives of military veterans or others with similar issues. This thesis presents a middleware solution for systems that work to detect, and with additional work possibly prevent, angry outbursts (also described in psychological literature as “rage”) using physiological sensor data and context-aware technology. This paper will cover a range of topics from methods for collecting system requirements for a subject group to the development of a social-context aware middleware. In doing such, the goal is to present a system that can be constructed and used in an in lab environment to further the research of building real-world systems that predict crisis events, setting the state for early intervention methods based on this approach

The Impact of Curriculum-Based Examinations on Learning in Canadian Secondary Schools

Externally set curriculum-based examinations at the end of high school apparently have pervasive backwash effects on middle school students, their parents, teachers and school administrators. Holding the social class background of students constant, students from Canadian provinces with examination systems were substantially (23 percent of a standard deviation) better prepared in mathematics and 18 percent of a standard deviation better prepared in science than students from provinces lacking such exams. The effect of an exam system on mathematics achievement of 13 year olds is larger in a standard deviation metric than the decline in math SAT scores between 1969 and 1980 that has been such a focus of public concern. Other natural experiments yield similar findings. When adjustments are made for ethnicity, gender and social class of SAT test takers, New York State ranks higher on the SAT than any of the other 38 states where the test is taken by large numbers of students. The mathematics and science achievement of Swedish high school seniors declined in the years following the elimination of high/medium stakes curriculum-based exams. The analysis also found that examination systems had pervasive effects on school administrators, teachers and parents. In the provinces with external exams, schools were more likely to: -- employ specialist teachers of mathematics and science -- employ teachers who had studied the subject in college, -- have high quality science laboratories -- schedule extra hours of math and science instruction -- assign more homework in math, in science and in other subjects -- have students do or watch experiments in science class and -- schedule frequent tests in math and science class. At home students watch less TV, spend more time reading for fun, and are more likely to report their parents want them to do well in math and science. In addition, parents are more likely to talk to their child about what they are learning at school

Teaching Science Lab Safety: Are Virtual Simulations Effective?

abstract: The purpose of this study was to investigate the impact of immersion on knowledge, cognitive load, and presence in a simulation designed to deliver a lesson on science lab safety training. 108 participants were randomly assigned to one of three conditions: high immersion (played an interactive simulation about lab safety in a VR headset), medium immersion (played the same interactive simulation on the computer), or low immersion (watched a video and read about lab safety procedures). Participants completed a pretest, a science lab safety training, a posttest (same as the pretest), a questionnaire with subjective presence questions, and a questionnaire with subjective cognitive load questions. Participants were again asked to complete a follow-up test (same as the pretest and posttest) a week later. The results revealed three significant findings: (a) Participants in the high and medium immersion conditions had significantly higher knowledge scores at posttest and follow-up than their peers in the low immersion condition, (b) Participants in the high and medium immersion conditions reported higher presence scores than participants in the low immersion conditions. (c) Correlation coefficients suggested that the higher the immersion and presence, the higher the knowledge scores are at posttest and follow-up. In addition, multiple hierarchical linear regression models were conducted out of which one was significant.Dissertation/ThesisDoctoral Dissertation Educational Technology 201

The Relationship Between Hegemonic Masculinity and Cognitive Thought Processes in Predicting Aggressive Behavior in Men

The purpose of this study was to explore whether there is a relationship between hegemonic masculinity and aggressive cognitive thought processes that ultimately end in aggressive behavior. In order to study this, I recruited male participants through Amazon MTurk, where 350 men completed a three-part survey and 344 were analyzed. First, participants took the Male Role Norms Inventory – Short Form (MRNI-SF), a sevenpoint Likert-type scale that measures seven traits commonly associated with masculinity. They then completed the Conditional Reasoning Test for Aggression (CRTA), which measures a person’s implicit thought process in order to see how likely they are to act aggressively in the future. Finally, the Buss-Perry Aggression Questionnaire Short Form (BPAQ-SF) was completed, another seven-point Likert-type scale that measures one’s self-perceived level of aggression. Through stepwise regression analysis it was found that, although cognitive thought processes still play a significant role in prediction aggression, masculinity norms are a better predictor overall

Human desire inference process and analysis

Ubiquitous computing becomes a more fascinating research area since it may offer us an unobtrusive way to help users in their environments that integrate surrounding objects and activities. To date, there have been numerous studies focusing on how user\u27s activity can be identified and predicted, without considering motivation driving an action. However, understanding the underlying motivation is a key to activity analysis. On the other hand, user\u27s desires often generate motivations to engage activities in order to fulfill such desires. Thus, we must study user\u27s desires in order to provide proper services to make the life of users more comfortable. In this study, we present how to design and implement a computational model for inference of user\u27s desire. First, we devised a hierarchical desire inference process based on the Bayesian Belief Networks (BBNs), that considers the affective states, behavior contexts and environmental contexts of a user at given points in time to infer the user\u27s desire. The inferred desire of the highest probability from the BBNs is then used in the subsequent decision making. Second, we extended a probabilistic framework based on the Dynamic Bayesian Belief Networks (DBBNs) which model the observation sequences and information theory. A generic hierarchical probabilistic framework for desire inference is introduced to model the context information and the visual sensory observations. Also, this framework dynamically evolves to account for temporal change in context information along with the change in user\u27s desire. Third, we described what possible factors are relevant to determine user\u27s desire. To achieve this, a full-scale experiment has been conducted. Raw data from sensors were interpreted as context information. We observed the user\u27s activities and get user\u27s emotions as a part of input parameters. Throughout the experiment, a complete analysis was conducted whereas 30 factors were considered and most relevant factors were selectively chosen using correlation coefficient and delta value. Our results show that 11 factors (3 emotions, 7 behaviors and 1 location factor) are relevant to inferring user\u27s desire. Finally, we have established an evaluation environment within the Smart Home Lab to validate our approach. In order to train and verify the desire inference model, multiple stimuli are provided to induce user\u27s desires and pilot data are collected during the experiments. For evaluation, we used the recall and precision methodology, which are basic measures. As a result, average precision was calculated to be 85% for human desire inference and 81% for Think-Aloud

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion