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"Lim, Ee-Peng"
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Computational trust models and machine learning
\"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches\"-- Provided by publisher.
Book
Leveraging large language models for career mobility analysis: a study of gender, race, and job change using U.S. online resume profiles
We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known as
FewSOC
that achieves accuracy significantly higher than the original occupation labels in the resume dataset. Analysis of 228,710 career trajectories reveals that intra-firm occupation change has been found to facilitate upward mobility most strongly, followed by inter-firm occupation change and inter-firm lateral move. Women and Black college graduates experience significantly lower returns from job changes than men and White peers. Multilevel sensitivity analyses confirm that these disparities are robust to cluster-level heterogeneity and reveal additional intersectional patterns.
Journal Article
Network data mining and analysis
\"Consider an online social networking site with millions of members in which members have the opportunity to befriend one another, send messages to each other, and post content on the site. Facebook, LinkedIn, and Twitter are examples of such sites. To make sense of data from these sites, we resort to social media mining to answer the following questions: 1. What are social communities in bipartite graphs and signed graphs? 2. How robust are the networks? How can we apply the robustness of networks? 3. How can we find identical social users across heterogeneous social networks? Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data\"-- Provided by publisher.
Book
Modeling location-based social network data with area attraction and neighborhood competition
Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to ascertain the two factors using three datasets gathered from users and venues of three major cities, Singapore, Jakarta and New York City. We then propose the visitation by area attractiveness and neighborhood competition (VAN) model incorporating area attraction and neighborhood competition factors. Our VAN model is also extended to incorporate social homophily so as to further enhance its modeling power. We evaluate VAN model using real world datasets against various state-of-the-art baselines. The results show that VAN model outperforms the baselines in check-in prediction task and its performance is robust under different parameter settings.
Journal Article
Non-binary evaluation of next-basket food recommendation
Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the offline evaluation experiments and binary relevance paradigm. Specifically, we argue that recommended baskets which are more similar to ground truth baskets are better recommendations than those that share little resemblance to the ground truth, and therefore, they should be granted some partial credits. Based on this notion of non-binary relevance assessment, we propose new evaluation metrics for NBR by adapting and extending similarity metrics from natural language processing (NLP) and text classification research. To validate the proposed metrics, we conducted two user studies on the next-meal food recommendation using numerous state-of-the-art NBR methods in both online and offline evaluation settings. Our findings show that the offline performance assessment based on the proposed non-binary evaluation metrics is more representative of the online evaluation performance than that of the standard evaluation metrics.
Journal Article
Talent Flow Analytics in Online Professional Network
Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data-driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework repurposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that the duration of a job’s existence (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform connectivity analysis at job and organization levels to derive insights on talent flow as well as job and organizational competitiveness.
Journal Article
A transformer framework for generating context-aware knowledge graph paths
Contextual Path Generation (CPG) refers to the task of generating knowledge path(s) between a pair of entities mentioned in an input textual context to determine the semantic connection between them. Such knowledge paths, also called contextual paths, can be very useful in many advanced information retrieval applications. Nevertheless, CPG involves several technical challenges, namely, sparse and noisy input context, missing relations in knowledge graphs, and generation of ill-formed and irrelevant knowledge paths. In this paper, we propose a transformer-based model architecture. In this approach, we leverage a mixture of pre-trained word and knowledge graph embeddings to encode the semantics of input context, a transformer decoder to perform path generation controlled by encoded input context and head entity to stay relevant to the context, and scaling methods to sample a well-formed path. We evaluate our proposed CPG models derived using the above architecture on two real datasets, both consisting of Wikinews articles as input context documents and ground truth contextual paths, as well as a large synthetic dataset to conduct larger-scale experiments. Our experiments show that our proposed models outperform the baseline models, and the scaling methods contribute to better quality contextual paths. We further analyze how CPG accuracy can be affected by different amount of context data, and missing relations in the knowledge graph. Finally, we demonstrate that an answer model for knowledge graph questions adapted for CPG could not perform well due to the lack of an effective path generation module.
Journal Article
On measuring network robustness for weighted networks
Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely used in many applications, such as information diffusion, disease transmission, and network security. However, existing metrics, including node connectivity, edge connectivity, and graph expansion, can be suboptimal for measuring network robustness since they are inefficient to be computed and cannot directly apply to the weighted networks or disconnected networks. In this paper, we define the R-energy as a new robustness measurement for weighted networks based on the method of spectral analysis. R-energy can cope with disconnected networks and is efficient to compute with a time complexity of O(|V|+|E|), where V and E are sets of vertices and edges in the network, respectively. Our experiments illustrate the rationality and efficiency of computing R-energy: (1) Removal of high degree vertices reduces network robustness more than that of random or small degree vertices; (2) it takes as little as 120 s to compute for a network with about 6M vertices and 33M edges. We can further detect events occurring in a dynamic Twitter network with about 130K users and discover interesting weekly tweeting trends by tracking changes to R-energy.
Journal Article
On detecting maximal quasi antagonistic communities in signed graphs
Many networks can be modeled as signed graphs. These include social networks, and relationships/interactions networks. Detecting sub-structures in such networks helps us understand user behavior, predict links, and recommend products. In this paper, we detect dense sub-structures from a signed graph, called quasi antagonistic communities (
QAC
s). An antagonistic community consists of two groups of users expressing positive relationships within each group but negative relationships across groups. Instead of requiring complete set of negative links across its groups, a
QAC
allows a small number of inter-group negative links to be missing. We propose an algorithm,
Mascot
, to find all maximal quasi antagonistic communities (
MQAC
s).
Mascot
consists of two stages: pruning and enumeration stages. Based on the properties of
QAC
, we propose four pruning rules to reduce the size of candidate graphs in the pruning stage. We use an enumeration tree to enumerate all
strongly connected subgraphs
in a top–down fashion in the second stage before they are used to construct
MQAC
s. We have conducted extensive experiments using synthetic signed graphs and two real networks to demonstrate the efficiency and accuracy of the
Mascot
algorithm. We have also found that detecting
MQAC
s helps us to predict the signs of links.
Journal Article
Understanding the determinants of human computation game acceptance
Purpose - Human computation games (HCGs) that blend gaming with utilitarian purposes are a potentially effective channel for content creation. The purpose of this paper is to investigate the driving factors behind players' adoption of HCGs through a music video tagging game. The effects of perceived aesthetic experience (PAE) and perceived output quality (POQ) on HCG acceptance are empirically examined. Design/methodology/approach - An integrative structural model is developed to explain how hedonic and utilitarian factors, including PAE and POQ, working with another salient factor - perceived usefulness (PU) - affect the acceptance of HCGs. The structural equation modeling method is used to verify the proposed model with data from 124 participants. Findings - Results show that PAE is the strongest predictor of HCGs adoption. PU has a significant impact on individuals' attitude toward HCGs. POQ is a salient predictor of PU and PAE, and its indirect effect on attitude is significance. Originality/value - From an academic point of view, this study provides a good understanding of the driving factors behind player acceptance of HCGs and adds new knowledge to games with utilitarian purposes. It is also one of the first to describe the components of game enjoyment with a taxonomy of aesthetic experiences. From the practical perspective, the investigation of the specific factors behind adoption of HCGs provides specific guidelines for their design and evaluation.
Journal Article