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 system’s 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.
Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users. Several group recommender systems have been ...proposed by extending traditional KNN based Collaborative Filtering. In this paper we explain how to perform group recommendations using Matrix Factorization (MF) based Collaborative Filtering (CF). We propose three original approaches to map the group of users to the latent factor space and compare the proposed methods in three different scenarios: when the group size is small, medium and large. We also compare the precision of the proposed methods with state-of-the-art group recommendation systems using KNN based Collaborative Filtering. We analyze group movie ratings on MovieLens and Netflix datasets. Our study demonstrates that the performance of group recommender systems varies depending on the size of the group, and MF based CF is the best option for group recommender systems.
This paper provides an innovative deep learning architecture to improve collaborative filtering results in recommender systems. It exploits the potential of the reliability concept to raise ...predictions and recommendations quality by incorporating prediction errors (reliabilities) in the deep learning layers. The underlying idea is to recommend highly predicted items that also have been found as reliable ones. We use the deep learning architecture to extract the existing non-linear relations between predictions, reliabilities, and accurate recommendations. The proposed architecture consists of three related stages, providing three stacked abstraction levels: (a) real prediction errors, (b) predicted errors (reliabilities), and (c) predicted ratings (predictions). In turn, each abstraction level requires a learning process: (a) Matrix Factorization from ratings, (b) Multilayer Neural Network fed with real prediction errors and hidden factors, and (c) Multilayer Neural Network fed with reliabilities and hidden factors. A complete set of experiments has been run involving three representative and open datasets and a state-of-the-art baseline. The results show strong prediction improvements and also important recommendation improvements, particularly for the recall quality measure.
This paper proposes a new method to solve the multistage security-constrained transmission expansion planning problem, incorporating lines based on high-voltage alternating current (HVAC) and ...high-voltage direct current (HVDC) alternatives. A novel mixed-integer linear programming model, which incorporates transmission losses using a piecewise linearization, is presented. An efficient method to reduce the search space of the problem is developed to help in the solution process. Garver's 6-bus system and a modified Southern Brazilian system are used to show the precision and efficiency of the proposed approach. The tests are performed for cases with and without HVDC links and transmission losses. The results indicate that better expansion plans can be found by considering HVDC proposals in the expansion process. The promising trend of using HVDC lines in future networks to improve the reliability in the system is demonstrated.
Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) ...obtain precise models using model-based solutions; 3) get accurate predictions; and 4) properly cluster elements. We propose the use of a Bayesian non-negative matrix factorization (BNMF) method to improve the current clustering results in the collaborative filtering area. We also provide an original pre-clustering algorithm adapted to the proposed probabilistic method. Results obtained using several open data sets show: 1) a conclusive clustering quality improvement when BNMF is used, compared with the classical matrix factorization or to the improved KMeans results; 2) a higher predictions accuracy using matrix factorization-based methods than using improved KMeans; and 3) better BNMF execution times compared with those of the classic matrix factorization, and an additional improvement when using the proposed pre-clustering algorithm.
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.
The saving and re-use of energy has acquired great relevance in recent years, being of great importance in the automotive sector. In the literature, it is possible to find different proposals for ...energy-harvesting damper systems (EHSA)—the electromagnetic damper being a highly recurrent but still poorly defined proposal. This article specifically focuses on studying the concept and feasibility of an electromagnetic suspension system that is capable of recovering energy, using a damper formed by permanent magnets and a system of coils that collect the electromotive force generated by the variation of the magnetic field. To study the feasibility of the system, it is necessary to know the maximum energy that can be recovered through the winding system; however, the difficulties in obtaining the derivative of the magnetic flux and its derivative for each position make the analytical method very tedious. This paper presents an experimental method with which to maximize energy recovery by defining the optimum relative position between magnet and coil.
Co-based amorphous microwires presenting the giant magnetoimpedance effect are proposed as sensing elements for high sensitivity biosensors. In this work we report an experimental method for ...contactless detection of stress, temperature, and liquid concentration with application in medical sensors using the giant magnetoimpedance effect on microwires in the GHz range. The method is based on the scattering of electromagnetic microwaves by FeCoSiB amorphous metallic microwires. A modulation of the scattering parameter is achieved by applying a magnetic bias field that tunes the magnetic permeability of the ferromagnetic microwires. We demonstrate that the OFF/ON switching of the bias activates or cancels the amorphous ferromagnetic microwires (AFMW) antenna behavior. We show the advantages of measuring the performing time dependent frequency sweeps. In this case, the AC-bias modulation of the scattering coefficient versus frequency may be clearly appreciated. Furthermore, this modulation is enhanced by using arrays of microwires with an increasing number of individual microwires according to the antenna radiation theory. Transmission spectra show significant changes in the range of 3 dB for a relatively weak magnetic field of 15 Oe. A demonstration of the possibilities of the method for biomedical applications is shown by means of wireless temperature detector from 0 to 100 °C.
In some real power systems in Latin America, such as those in Brazil, Colombia and Ecuador, the final decision about when to carry out the preventive maintenance is made by the ISO (integrated system ...operator), in a centralized way. In such a scenario, an optimization model coupling generation maintenance scheduling (GMS) and hydrothermal dispatch (HTD) might lead to lower operation costs from the ISO’s perspective, leading to an optimal solution that might differ from the combined base maintenance schedule (BMS) proposed by the power generation companies. In this paper we develop a mathematical model coordinating the GMS with middle term HTD from the point of view of the ISO. The mathematical model proposed is a mixed integer linear programming (MILP) type that approaches the uncertainty in water inflows through multiple scenarios. The test system emulates a real scale hydrothermal system. The results obtained by the proposed model show operation cost reduction ranging from 5% to as much as 20%, when compared with the BMS proposed by the individual companies.
Unveiling the Hidden Entropy in ZnFe2O4 Cobos, Miguel Angel; Hernando, Antonio; Marco, José Francisco ...
Materials,
02/2022, Letnik:
15, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The antiferromagnetic (AFM) transition of the normal ZnFe2O4 has been intensively investigated with results showing a lack of long-range order, spin frustrations, and a “hidden” entropy in the ...calorimetric properties for inversion degrees δ ≈ 0 or δ = 0. As δ drastically impacts the magnetic properties, it is logical to question how a δ value slightly different from zero can affect the magnetic properties. In this work, (Zn1-δFeδ)ZnδFe2-δO4 with δ = 0.05 and δ = 0.27 have been investigated with calorimetry at different applied fields. It is shown that a δ value as small as 0.05 may affect 40% of the unit cells, which become locally ferrimagnetic (FiM) and coexists with AFM and spin disordered regions. The spin disorder disappears under an applied field of 1 T. Mossbauer spectroscopy confirms the presence of a volume fraction with a low hyperfine field that can be ascribed to these spin disordered regions. The volume fractions of the three magnetic phases estimated from entropy and hyperfine measurements are roughly coincident and correspond to approximately 1/3 for each of them. The “hidden” entropy is the zero point entropy different from 0. Consequently, the so-called “hidden” entropy can be ascribed to the frustrations of the spins at the interphase between the AFM-FiM phases due to having δ ≈ 0 instead of ideal δ = 0.
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