Antonio Hernando

A collaborative filtering approach to mitigate the new user cold start problem

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender systems collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation.

Collaborative filtering adapted to recommender systems of e-learning

In the context of e-learning recommender systems, we propose that the users with greater knowledge (for example, those who have obtained better results in various tests) have greater weight in the calculation of the recommendations than the users with less knowledge. To achieve this objective, we have designed some new equations in the nucleus of the memory-based collaborative filtering, in such a way that the existent equations are extended to collect and process the information relative to the scores obtained by each user in a variable number of level tests.

Induced magnetic anisotropy and change of the magnetostriction by current annealing in Co-based amorphous alloys

Abstract Magnetic anisotropies can be induced in amorphous alloys by different treatments such as cold rolling and stress or field anneal. In this work, we present the experimental results concerning the magnetic anisotropy induced by transverse field annealing. Here, the magnetic field has been created by an electrical current flowing along the amorphous ribbon. This current produces simultaneously an increase of the temperature of the sample and the field necessary to induce the transverse anisotropy. This method is convenient due to the rapidity in obtaining the equilibrium annealing temperature and the simplification of the experimental set-up. However inhomogeneity in both annealing temperature and applied field cannot be avoided. The kinetics exhibit a similar behaviour to that observed by using the conventional magnetic annealing method. Correlated changes of the magnetostriction are also presented. It is to be noted that these changes do not affect the compensation temperature. These investigations have been carried out for two Co-based amorphous alloys whose nominal compositions are (Co0.95Fe0.05)75Si10B15 and (Co0.92Fe0.08)75Si15B10.

Improving collaborative filtering recommender system results and performance using genetic algorithms

This paper presents a metric to measure similarity between users, which is applicable in collaborative filtering processes carried out in recommender systems. The proposed metric is formulated via a simple linear combination of values and weights. Values are calculated for each pair of users between which the similarity is obtained, whilst weights are only calculated once, making use of a prior stage in which a genetic algorithm extracts weightings from the recommender system which depend on the specific nature of the data from each recommender system. The results obtained present significant improvements in prediction quality, recommendation quality and performance.

A collaborative filtering similarity measure based on singularities

Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calculate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.

A high-performance hysteresis loop tracer

A high‐performance and inexpensive hysteresis loop tracer has been developed to measure quasistatic (0.02 Hz or less) hysteresis loops of soft ferromagnetic materials. It was applied very successfully to measure straight pieces of amorphous and nanocrystalline ribbons and amorphous wires. Especially high‐magnetic‐field resolution is required when nanocrystalline ferromagnets and amorphous wires are measured. Nanocrystalline materials exhibit very low coercivity (Hc=0.1–0.5 A/m). The error of Hc measurement using this tracer does not exceed 0.05 A/m even though the amorphous wires have very small cross section (0.008 mm2). The examples of hysteresis loops measured at low (50 A/m) and high magnetic field (14 kA/m) are presented. The apparatus consists of an IBM‐compatible computer equipped with 12 bit analog‐to‐digital and digital‐to‐analog converters, bipolar power supply, fluxmeter, solenoid and a pickup coil connected to a compensation coil. This equipment is free of 50 Hz noise, a significant problem in...

Evidence of spin disorder at the surface–core interface of oxygen passivated Fe nanoparticles

Hysteresis, thermal dependence of magnetization, and coercivity of oxide coated ultrafine Fe particles prepared by inert gas condensation and oxygen passivation have been studied in the 5–300 K range. The results are found to be consistent with a spin-glasslike state of the oxide layer inducing, through exchange interaction with the ferromagnetic core, a shift of the field cooled hysteresis loops at temperatures below the freezing at approximately 50 K.

Modification of the saturation magnetostriction constant after thermal treatments for the Co58Fe5Ni10B16Si11 amorphous ribbon

Abstract A new method for measuring the magnetostriction constant has been developed. The resolution of this sensitive method allows to measure values of that constant as low as 10 -9 . It has been found that after thermal treatments, the magnetostriction constant of the amorphous ribbon whose nominal composition is Co 58 Fe 5 Ni 10 B 16 Si 11 changes not only its absolute value but also its sign. The kinetics of this variation has also been studied and a mean value of the activation energy of 0.8 eV for the relaxation process was obtained. Such variations of the magnetostriction constant are discussed as produced by modifications of the short-range order during annealing.

Collaborative filtering based on significances

It seems reasonable to think that there may be some items and some users in a recommender system that could be highly significant in making recommendations. For instance, the recent and much-advertised Apple product may be regarded as more significant compared with an outdated MP3 device (which is still on sale). In this paper, we introduce a new method to improve the information used in collaborative filtering processes by weighting the ratings of the items according to their importance. We provide here a formalisation of the collaborative filtering process based on the concept of significance. In this way, the k-neighbours are calculated taking into account the ratings of the items, the significance of the items and the significance of each user for making recommendations to other users. This formalisation includes extensions of the concepts related to similarity measures and prediction/recommendation quality measures. We will show also the results obtained from a set of experiments using Movielens and Netflix. The results confirm the advantage of introducing the concept of significance in general recommender systems and especially in recommender systems in which it is easy to determine the relative importance of the items: for example, most widely sold products in e-commerce, most widely commented news items in web-news, most widely watched programs on TV, and the latest sports champions.

A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model

In this paper we present a novel technique for predicting the tastes of users in recommender systems based on collaborative filtering. Our technique is based on factorizing the rating matrix into two non negative matrices whose components lie within the range 0, 1 with an understandable probabilistic meaning. Thanks to this decomposition we can accurately predict the ratings of users, find out some groups of users with the same tastes, as well as justify and understand the recommendations our technique provides.