A dyadic segmentation approach to business partnerships

In business science, the studied objects are often groups of partners rather than independent firms. Extending classical segmentation to these polyads raises conceptual problems, principally: defining what should be considered as common or specific at the partners' and at the segment levels. The general approaches consist either in merging partners characteristics and performances into a single macro-object, thus loosing their specific contributions to each partner's performance, or in modelling partners' performance as if their models were independent. As a step to understanding, how partnership influences firms' performance, the dyadic (i.e. two partners') case is studied. First, some theoretical issues concerning the degrees of individual and contributive interest in a dyadic population are discussed. Next, partnership's conceptualisation is based upon two models for each firm: a "self-model" that reflects how the firm's characteristics explain its own performance, and a "contributive-model" model that reflects how these characteristics influence the partner's performance. This allows definition of three relationship modes: merging, teaming and sharing. Subsequently, dyad segmentation strategies are discussed according to their capacity to reflect the modes of partnership and a dyadic clusterwise regression method, based on a genetic algorithm, is presented. Finally, the method is illustrated empirically using actual data of business partners in the software market.Jacques-Marie Aurifeille and Christopher John Medli

Generalized Clusterwise Regression for Simultaneous Estimation of Optimal Pavement Clusters and Performance Models

The existing state-of-the-art approach of Clusterwise Regression (CR) to estimate pavement performance models (PPMs) pre-specifies explanatory variables without testing their significance; as an input, this approach requires the number of clusters for a given data set. Time-consuming ‘trial and error’ methods are required to determine the optimal number of clusters. A common objective function is the minimization of the total sum of squared errors (SSE). Given that SSE decreases monotonically as a function of the number of clusters, the optimal number of clusters with minimum SSE always is the total number of data points. Hence, the minimization of SSE is not the best objective function to seek for an optimal number of clusters. In previous studies, the PPMs were restricted to be either linear or nonlinear, irrespective of which functional form provided the best results. The existing mathematical programming formulations did not include constraints that ensured the minimum number of observations required in each cluster to achieve statistical significance. In addition, a pavement sample could be associated with multiple performance models. Hence, additional modeling was required to combine the results from multiple models. To address all these limitations, this research proposes a generalized CR that simultaneously 1) finds the optimal number of pavement clusters, 2) assigns pavement samples into clusters, 3) estimates the coefficients of cluster-specific explanatory variables, and 4) determines the best functional form between linear and nonlinear models. Linear and nonlinear functional forms were investigated to select the best model specification. A mixed-integer nonlinear mathematical program was formulated with the Bayesian Information Criteria (BIC) as the objective function. The advantage of using BIC is that it penalizes for including additional parameters (i.e., number of clusters and/or explanatory variables). Hence, the optimal CR models provided a balance between goodness of fit and model complexity. In addition, the search process for the best model specification using BIC has the property of consistency, which asymptotically selects this model with a probability of ‘1’. Comprehensive solution algorithms – Simulated Annealing coupled with Ordinary Least Squares for linear models and All Subsets Regression for nonlinear models – were implemented to solve the proposed mathematical problem. The algorithms selected the best model specification for each cluster after exploring all possible combinations of potentially significant explanatory variables. Potential multicollinearity issues were investigated and addressed as required. Variables identified as significant explanatory variables were average daily traffic, pavement age, rut depth along the pavement, annual average precipitation and minimum temperature, road functional class, prioritization category, and the number of lanes. All these variables were considered in the literature as the most critical factors for pavement deterioration. In addition, the predictive capability of the estimated models was investigated. The results showed that the models were robust without any overfitting issues, and provided small prediction errors. The models developed using the proposed approach provided superior explanatory power compared to those that were developed using the existing state-of-the-art approach of clusterwise regression. In particular, for the data set used in this research, nonlinear models provided better explanatory power than did the linear models. As expected, the results illustrated that different clusters might require different explanatory variables and associated coefficients. Similarly, determining the optimal number of clusters while estimating the corresponding PPMs contributed significantly to reduce the estimation error

Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.algorithms;probabilistic latent semantic analysis;hybrid recommender systems;recommender systems

Mining Data with Feature Interactions

abstract: Models using feature interactions have been applied successfully in many areas such as biomedical analysis, recommender systems. The popularity of using feature interactions mainly lies in (1) they are able to capture the nonlinearity of the data compared with linear effects and (2) they enjoy great interpretability. In this thesis, I propose a series of formulations using feature interactions for real world problems and develop efficient algorithms for solving them. Specifically, I first propose to directly solve the non-convex formulation of the weak hierarchical Lasso which imposes weak hierarchy on individual features and interactions but can only be approximately solved by a convex relaxation in existing studies. I further propose to use the non-convex weak hierarchical Lasso formulation for hypothesis testing on the interaction features with hierarchical assumptions. Secondly, I propose a type of bi-linear models that take advantage of interactions of features for drug discovery problems where specific drug-drug pairs or drug-disease pairs are of interest. These models are learned by maximizing the number of positive data pairs that rank above the average score of unlabeled data pairs. Then I generalize the method to the case of using the top-ranked unlabeled data pairs for representative construction and derive an efficient algorithm for the extended formulation. Last but not least, motivated by a special form of bi-linear models, I propose a framework that enables simultaneously subgrouping data points and building specific models on the subgroups for learning on massive and heterogeneous datasets. Experiments on synthetic and real datasets are conducted to demonstrate the effectiveness or efficiency of the proposed methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Least-squares Bilinear Clustering of Three-way Data

__Abstract__ A least-squares bilinear clustering framework for modelling three-way data, where each observation consists of an ordinary two-way matrix, is introduced. The method combines bilinear decompositions of the two-way matrices into overall means, row margins, column margins and row-column interactions with clustering along the third way. Different clusterings are defined for each part of the decomposition, so that up to four different classifications are defined jointly. The computational burden is greatly reduced by the orthogonality of the bilinear model, such that the joint clustering problem reduces to separate ones which can be handled independently. Three of these sub-problems are specific cases of $k$-means clustering; a special algorithm is formulated for the row-column interactions, which are displayed in clusterwise biplots. The method is illustrated via two empirical examples and interpreting the interaction biplots are discussed

Cortical thinning correlates of changes in visuospatial and visuoperceptual performance in Parkinson's disease: A 4-year follow-up

Background. Growing evidence highlights the relevance of posterior cortically-based cognitive deficits in Parkinson's disease (PD) as possible biomarkers of the evolution to dementia. Cross-sectional correlational studies have established a relationship between the degree of atrophy in posterior brain regions and visuospatial and visuoperceptual (VS/VP) impairment. The aim of this study is to address the progressive cortical thinning correlates of VS/VP performance in PD. Methods. Forty-four PD patients and 20 matched healthy subjects were included in this study and followed for 4 years. Tests used to assess VS/VP functions included were: Benton's Judgement of Line Orientation (JLOT), Facial Recognition (FRT), and Visual Form Discrimination (VFDT) Tests; Symbol Digit Modalities Test (SDMT); and the Pentagon Copying Test (PCT). Structural magnetic resonance imaging data and FreeSurfer were used to evaluate cortical thinning evolution. Results. PD patients with normal cognition (PD-NC) and PD patients with mild cognitive impairment (PD-MCI) differed significantly in the progression of cortical thinning in posterior regions. In PD-MCI patients, the change in VS/VP functions assessed by PCT, JLOT, FRT, and SMDT correlated with the symmetrized percent change of cortical thinning of occipital, parietal, and temporal regions. In PD-NC patients, we also observed a correlation between changes in FRT and thinning in parieto-occipital regions. Conclusion. In this study, we establish the neuroanatomical substrate of progressive changes in VS/VP performance in PD patients with and without MCI. In agreement with cross-sectional data, VS/VP changes over time are related to cortical thinning in posterior regions

Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.Comment: Datasets of this study can be freely downloaded through: https://doi.org/10.6084/m9.figshare.4765150.v

THE EFFECT OF ASTROLOGY ON YOUNG CUSTOMER BEHAVIORS

The main purpose of this study was to examine the effect of date-of-birth on consumption behaviors of young people. A face-to-face interview survey is conducted to collect data. SPSS 18.0 for Windows was used for data analysis. Descriptive statistics such as means, frequencies, ANOVA tests and Chi-square tests were calculated. The findings pointed out that the young consumers on fire group (Aries, Leo, and Sagittarius) take more instant and impulsive purchase actions. It is a new study about the effect of astrology on young customer behaviors. It presents valuable information that can assist marketers to understand young customers’ consumption behaviors.Astrology, Marketing, Young Consumers, Consumer Behaviors, Buying Behaviors

Social perspective taking is associated with self-reported prosocial behavior and regional cortical thickness across adolescence

Basic perspective taking and mentalising abilities develop in childhood, but recent studies indicate that the use of social perspective taking to guide decisions and actions has a prolonged development that continues throughout adolescence. Here, we aimed to replicate this research and investigate the hypotheses that individual differences in social perspective taking in adolescence are associated with real-life prosocial and antisocial behavior and differences in brain structure. We employed an experimental approach and a large cross-sectional sample (n=293) of participants aged 7-26 years old to assess age-related improvement in social perspective taking usage during performance of a version of the Director task. In subsamples, we then tested how individual differences in social perspective taking were related to self-reported prosocial behavior and peer relationship problems on the Strengths and Difficulties Questionnaire (SDQ) (n=184) and to magnetic resonance imaging (MRI) measures of regional cortical thickness and surface area (n=226). The pattern of results in the Director task replicated previous findings by demonstrating continued improvement in use of social perspective taking across adolescence. The study also showed that better social perspective taking usage is associated with more self-reported prosocial behavior, as well as to thinner cerebral cortex in regions in the left hemisphere encompassing parts of the caudal middle frontal and precentral gyri and lateral parietal regions. These associations were observed independently of age, and might partly reflect individual developmental variability. The relevance of cortical development was additionally supported by indirect effects of age on social perspective taking usage via cortical thickness