Reducing Offline Evaluation Bias in Recommendation Systems

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Memory for prices and the euro cash changeover: An analysis for cinema prices in Italy

The question addressed by this study is whether consumers remember past prices correctly. We test Italian citizensÂ’ memory for cinema prices with questionnaires distributed to moviegoers. The analysis concentrates on the memory of pre-euro prices, but the recall for a more recent period is also investigated. The results show that only a small percentage of respondents recalled the correct price, and that the average prices recalled were much lower than the actual pre-euro prices and dated back to years before the changeover. Price recall is less accurate for the respondents who perceive higher and more persistent inflation; it is also worse for the older respondents and for the less frequent movie-goers.prices, memory, perceptions, euro

Using the Mean Absolute Percentage Error for Regression Models

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimization remains possible using the MAPE instead of the MAE.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

Reducing offline evaluation bias of collaborative filtering algorithms

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

Consistance de la minimisation du risque empirique pour l'optimisation de l'erreur relative moyenne

National audienceWe study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We also show that, under some asumptions, universal consistency of Empirical Risk Minimization remains possible using the MAPE.Nous nous intéressons au problème de la minimisation de l'erreur relative moyenne dans le cadre des modèles de régression. Nous montrons que l'optimisation de ce critère est équivalente à la minimisation de l'erreur absolue par régressions pondérées et que l'approche par minimisation du risque empirique est, sous certaines hypothèses, consistante pour la minimisation de ce critère

Memory for symmetry and perceptual binding in patients with schizophrenia

The present study investigated the use of perceptual binding processes in schizophrenic (SC) patients and matched healthy controls, by examining their performance on the recall of symmetrical (vertical, horizontal and diagonal) and asymmetrical patterns varying in length between 2 and 9 items. The results showed that, although SC patients were less accurate than controls in all conditions, both groups recalled symmetrical patterns better than asymmetrical ones. The impairment of SC patients was magnified with supra-span symmetrical arrays, and they were more likely to reproduce symmetrical patterns as asymmetrical, particularly at medium and high length levels. Hierarchical regression analyses further indicated that the between-group differences in the recall of supra-span vertical and horizontal arrays, which require a greater involvement of visual pattern processes, remained significant after removing the variance associated with performance on asymmetrical patterns, which primarily reflects intrafigural spatial processes. It is proposed that schizophrenia may be associated with a specific deficit in the formation and retrieval of the global visual images of studied patterns and in the use of the on-line information about the type of symmetry being tested to guide retrieval processes. © 2013 Elsevier B.V

Memory for object location: A span study in children

The aim of the present study was to analyze the developmental changes in three spatial processes, namely, in positional reconstruction involving the retention of spatial locations per se (Positional encoding task), in the assignment of objects to positions (Object-to-position assignment task), and in the integration of these two (Combined task). A span procedure was used to assess the development of spatial memory in children aged 6, 8, and 10 years tested in these three tasks. The findings of the present study provide developmental spans for each relocation task. Results show an age-dependent improvement in all tasks, suggesting that spatial position is not automatically encoded. The results also show different developmental patterns for the relocation tasks considered, Suggesting that spatial memory comprises a number of different component processes

Análisis de una cadena de inversores asimétricos como elemento de retardo

El presente trabajo estudia una cadena de inversores asimétricos a ser empleada como elemento de retardo en circuitos integrados. Se desarrolla un estudio analítico que permite hallar una fórmula aproximada para el diseño, a la vez que deja ver que parámetros y cómo influyen en el tiempo de retardo. Nos interesa en particular generar retardos de tiempo independientes de variaciones en el proceso de fabricación. En este sentido la cadena de inversores asimétricos muestra ser poco sensible a variaciones en el VT de los transistores. La cadena de inversores asimétricos demuestra ser una solución eficiente en área y con una menor dispersión del tiempo de retardo en función de los parámetros de la tecnología, en comparación con otra clase de retardos sencillos analizados