Freddy Chong Tat Chua

Generative Models for Item Adoptions Using Social Correlation

Users face many choices on the web when it comes to choosing which product to buy, which video to watch, and so on. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation, which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: Whether the items adopted by a user correlate with items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation framework that considers a social correlation matrix representing the degrees of correlation from every user to the users friends, in addition to a set of latent factors representing topics of interests of individual users. Based on the framework, we develop two generative models, namely sequential and unified, and the corresponding parameter estimation approaches. From each model, we devise the social correlation only and hybrid methods for predicting missing adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed models outperform the approach based on latent factors only (LDA).

Trust network inference for online rating data using generative models

In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF) Model, a novel probabilistic model that generate ratings based on a number of raters and contributors factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.

Predicting Item Adoption Using Social Correlation

Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to incorporate social correlation in order to improve prediction of unobserved item adoptions. We propose the Social Correlation model based on Latent Dirichlet Allocation (LDA) that decomposes the adoption graph into a set of latent factors reflecting user preferences, and a social correlation matrix reflecting the degree of correlation from one user to another. This matrix is learned (rather than pre-assigned), has probabilistic interpretation, and preserves the underlying social network structure. We further devise a Hybrid model that combines a user’s own latent factors with her friends’ for adoption prediction. Experiments on Epinions and LiveJournal data sets show that our proposed models outperform the approach based on latent factors only (LDA).

Structural analysis in multi-relational social networks

Modern social networks often consist of multiple relations among individuals. Understanding the structure of such multi-relational network is essential. In sociology, one way of structural analysis is to identify different positions and roles using blockmodels. In this paper, we generalize stochastic blockmodels to Generalized Stochastic Blockmodels (GSBM) for performing positional and role analysis on multi-relational networks. Our GSBM generalizes many different kinds of Multivariate Probability Distribution Function (MVPDF) to model different kinds of multi-relational networks. In particular, we propose to use multivariate Poisson distribution for multi-relational social networks. Our experiments show that GSBM is able to identify the structures for both synthetic and real world network data. These structures can further be used for predicting relationships between individuals.

Community-based classification of noun phrases in twitter

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the authors behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.

Messaging Behavior Modeling in Mobile Social Networks

Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message and responding to an incoming message. We propose various models for the two behaviors also known as engagingness and responsiveness. Our experiments show that the two behaviors are quite distinct from each other although they may be correlated. We also show that engaging and responsive users enjoy more friendships. Finally, we show that the engaging and responsive users participate more in messaging about major topics.

Mining Business Competitiveness from User Visitation Data

Ranking businesses by competitiveness is useful in many applications including business (e.g., restaurant) recommendation, and estimation of intrinsic value of businesses for mergers and acquisitions. Our literature reveals that previous methods of business ranking have ignored the competing relationship among businesses within their geographical areas. To account for competition, we propose the use of PageRank model and its variant to derive the Competitive Rank of businesses. We use the check-ins of users from Foursquare, a location-based social network, to model the winners of competitions among stores. The results of our experiments show that Competitive Rank works well when evaluated against ground truth business ranking.

Modeling Bipartite Graphs Using Hierarchical Structures

Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preserve the network fidelity as well as the assortative and disassortative structures of bipartite networks. We apply the proposed model on some paper-author networks in DBLP to find their optimal hierarchical structures. Using the optimal bipartite hierarchical structure, we regenerate networks that exhibit the similar network properties and degree distribution as the observed networks.

Mining Social Dependencies in Dynamic Interaction Networks

User-to-user interactions have become ubiquitous in Web 2.0. Users exchange emails, post on newsgroups, tag web pages, co-author papers, etc. Through these interactions, users co-produce or co-adopt content items (e.g., words in emails, tags in social bookmarking sites). We model such dynamic interactions as a user interaction network, which relates users, interactions, and content items over time. After some interactions, a user may produce content that is more similar to those produced by other users previously. We term this effect social dependency, and we seek to mine from such networks the degree to which a user may be socially dependent on another user over time. We propose a Decay Topic Model to model the evolution of a user’s preferences for content items at the topic level, as well as a Social Dependency Metric that quantifies the extent of social dependency based on interactions and content changes. Our experiments on two user interaction networks induced from real-life datasets show the effectiveness of our approach.