Learning rating patterns for Top-N recommendations

Two rating patterns exist in the user × item rating matrix and influence each other: the personal rating patterns are hidden in each user's entire rating history, while the global rating patterns are hidden in the entire user × item rating matrix. In this paper, a Rating Pattern Subspace is proposed to model both of the rating patterns simultaneously by iteratively refining each other with an EM-like algorithm. Firstly, a low-rank subspace is built up to model the global rating patterns from the whole user × item rating matrix, then, the projection for each user on the subspace is refined individually based on his/her own entire rating history. After that, the refined user projections on the subspace are used to improve the modelling of the global rating patterns. Iteratively, we can obtain a well-trained low-rank Rating Pattern Subspace, which is capable of modelling both the personal and the global rating patterns. Based on this subspace, we propose a RapSVD algorithm to generate Top-N recommendations, and the experiment results show that the proposed method can significantly outperform the other state-of-the-art Top-N recommendation methods in terms of accuracy, especially on long tail item recommendations.

A learning method for Top-N recommendations with incomplete data

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.

Análisis del mercado del rating de las emisiones de titulación en España (1993-2011)

[ES] La calificación crediticia externa es un elemento clave para el buen fin de las operaciones de titulización. No obstante, las agencias de calificación han sido objeto de una intensa controversia, cuestionándose su fiabilidad y objetividad. En este trabajo abundamos en esta cuestión, analizando la distribución de los ratings otorgados a las emisiones de bonos de titulización llevadas a cabo en España (1993-2011), uno de los países más activos en cuanto al volumen de emisiones, llegaron a ocupar el segundo puesto a nivel europeo y el tercero a nivel mundial. Aportamos evidencia sobre ciertas anomalías entre las que cabe destacar la estructura oligopolística del mercado del rating, la fragilidad desde una perspectiva histórica de las calificaciones otorgadas, y la existencia de patrones no homogéneos en la elección de la agencia de calificación, tanto si se tiene en cuenta la sociedad gestora, como el colateral de respaldo.

Patterns and factors associated with low adherence to psychotropic medications during pregnancy-a cross-sectional, multinational web-based study.

BACKGROUND: No previous studies have explored how closely women follow their psychotropic drug regimens during pregnancy. This study aimed to explore patterns of and factors associated with low adherence to psychotropic medication during pregnancy. METHODS: Multinational web-based study was performed in 18 countries in Europe, North America, and Australia. Uniform data collection was ensured via an electronic questionnaire. Pregnant women were eligible to participate. Adherence was measured via the 8-item Morisky Medication Adherence Scale (MMAS-8). The Beliefs about Prescribed Medicines Questionnaire (BMQ-specific), the Edinburgh Postnatal Depression Scale (EPDS), and a numeric rating scale were utilized to measure women's beliefs, depressive symptoms, and antidepressant risk perception, respectively. Participants reporting use of psychotropic medication during pregnancy (n = 160) were included in the analysis. RESULTS: On the basis of the MMAS-8, 78 of 160 women (48.8%, 95% CI: 41.1-56.4%) demonstrated low adherence during pregnancy. The rates of low adherence were 51.3% for medication for anxiety, 47.2% for depression, and 42.9% for other psychiatric disorders. Smoking during pregnancy, elevated antidepressant risk perception (risk≥6), and depressive symptoms were associated with a significant 3.9-, 2.3-, and 2.5-fold increased likelihood of low medication adherence, respectively. Women on psychotropic polytherapy were less likely to demonstrate low adherence. The belief that the benefit of pharmacotherapy outweighed the risks positively correlated (r = .282) with higher medication adherence. CONCLUSIONS: Approximately one of two pregnant women using psychotropic medication demonstrated low adherence in pregnancy. Life-style factors, risk perception, depressive symptoms, and individual beliefs are important factors related to adherence to psychotropic medication in pregnancy.

Obsessive-compulsive disorder: Influence of age at onset on comorbidity patterns

Purpose: This study investigates the influence of age at onset of OCS on psychiatric comorbidities, and tries to establish a cut-off point for age at onset. Methods: Three hundred and thirty OCD patients were consecutively recruited and interviewed using the following structured interviews: Yale-Brown Obsessive Compulsive Scale; Yale Global Tic Severity Scale and the Structured Clinical Interview for DSM-IV. Data were analyzed with regression and cluster analysis. Results: Lower age at onset was associated with a higher probability of having comorbidity with tic, anxiety, somatoform, eating and impulse-control disorders. Longer illness duration was associated with lower chance of having tics. Female gender was associated with anxiety, eating and impulse-control disorders. Tic disorders were associated with anxiety disorders and attention-deficit/hyperactivity disorder. No cut-off age at onset was found to clearly divide the sample in homogeneous subgroups. However, cluster analyses revealed that differences started to emerge at the age of 10 and were more pronounced at the age of 17, suggesting that these were the best cut-off points on this sample. Conclusions: Age at onset is associated with specific comorbidity patterns in OCD patients. More prominent differences are obtained when analyzing age at onset as an absolute value. © 2008 Elsevier Masson SAS. All rights reserved.

Evaluating the external validity of comorbid disorders in youth using rating scales and behavior observations

To evaluate the theoretical underpinnings of current categorical approaches to classify childhood psychopathological conditions, this dissertation examined whether children with a single diagnosis of an anxiety disorder (ANX only) and children with an anxiety diagnosis comorbid with other diagnoses (i.e., anxiety + anxiety disorder [ANX + ANX], anxiety + depressive disorder [ANX + DEP], and anxiety + disruptive disorder [ANX + EXT]) could be differentiated using external validation criteria of clinical phenomenology (i.e., levels of anxiety, depression, and internalizing, externalizing and total behavior problems). This study further examined whether the four groups could be differentiated in terms of their interaction patterns with their parents and peers, respectively. The sample consisted of 129 youth and their parents who presented to the Child Anxiety and Phobia Program (CAPP) housed within the Child and Family Psychosocial Research Center at Florida International University, Miami. Youth were between the ages of 8 and 14 years old. A battery of questionnaires was used to assess participants' clinical presentation in terms of levels of anxiety, depression, and internalizing and externalizing symptoms. Family and peer interaction were evaluated through rating scales and through behavior observation tasks. Statistics based on the parameter estimates of the structured equation models indicated that all the comorbid groups were significantly different from the pure anxiety disorder group when it came to depression indices of clinical phenomenology. Further, significant differences appeared mainly in terms of the ANX + DEP comorbid group relative to the other comorbid groups. In terms of Parent-child interaction the ANX + EXT and the ANX + DEP comorbid groups were differentiated from the pure anxiety disorder and ANX + ANX comorbid group when it came to the appraisal of the parent/child relationship by the parent, and the acceptance subscale according to the mother report. In terms of peer-child interaction the ANX + EXT and the ANX + DEP comorbid groups were statistically significantly different from the pure anxiety disorder only when it came to the positive interactions and the social skills as rated by mother. Limitations and future research recommendations are discussed.

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

The Self as a Moral Basis: Do Our Own Patterns of Behavior Affect our Moral Attributions?

To determine which actions are morally acceptable, psychologists typically focus on decision making within existing moral paradigms. However, this fails to comment upon individual and social processes, such as attribution, that determine morality. To address these processes, this study had participants respond to morally-charged scenarios by rating the immorality of an actor who did not tip a waiter (n = 125), was partial to infidelity (n = 128), and texted while driving (n = 128). Participants also completed an empathy measure, and provided their own frequency of engaging in certain behaviors, including those featured in the scenarios. Immorality ratings were compared to the participants’ own frequency of the scenario action (hypothesized to lower ratings), as well as empathy and outcome severity (both hypothesized to increase ratings). Findings were assessed in three regressions, one per scenario. Behavioral similarity predicted immorality ratings in each (p ≤ .03), empathy predicted ratings only for not tipping a waiter (p = .04), while outcome severity was un-predictive in each scenario. Theoretical implications, directions for future research, and limitations of the study are discussed.

Quantitative electroencephalographic comodulation : an investigation of patterns in chronic fatigue syndrome

Introduction. This is a pilot study of quantitative electro-encephalographic (QEEG) comodulation analysis, which is used to assist in identifying regional brain differences in those people suffering from chronic fatigue syndrome (CFS) compared to a normative database. The QEEG comodulation analysis examines spatial-temporal cross-correlation of spectral estimates in the resting dominant frequency band. A pattern shown by Sterman and Kaiser (2001) and referred to as the anterior posterior dissociation (APD) discloses a significant reduction in shared functional modulation between frontal and centro-parietal areas of the cortex. This research attempts to examine whether this pattern is evident in CFS. Method. Eleven adult participants, diagnosed by a physician as having CFS, were involved in QEEG data collection. Nineteen-channel cap recordings were made in five conditions: eyes-closed baseline, eyes-open, reading task one, math computations task two, and a second eyes-closed baseline. Results. Four of the 11 participants showed an anterior posterior dissociation pattern for the eyes-closed resting dominant frequency. However, seven of the 11 participants did not show this pattern. Examination of the mean 8-12 Hz amplitudes across three cortical regions (frontal, central and parietal) indicated a trend of higher overall alpha levels in the parietal region in CFS patients who showed the APD pattern compared to those who did not have this pattern. All patients showing the pattern were free of medication, while 71% of those absent of the pattern were using antidepressant medications. Conclusions. Although the sample is small, it is suggested that this method of evaluating the disorder holds promise. The fact that this pattern was not consistently represented in the CFS sample could be explained by the possibility of subtypes of CFS, or perhaps co-morbid conditions. Further, the use of antidepressant medications may mask the pattern by altering the temporal characteristics of the EEG. The results of this pilot study indicate that further research is warranted to verify that the pattern holds across the wider population of CFS sufferers.