Recommender systems and time context: Characterization of a robust evaluation protocol to increase reliability of measured improvements

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

Temporal rating habits: A valuable tool for rating discrimination

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAMRa '11 Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, http://dx.doi.org/10.1145/2096112.2096118.In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) to tackle the Identifying Ratings (track 2) task of the CAMRa 2011 Challenge. The experiments performed include time-frequency probabilistic strategies, heuristic collaborative filtering (CF) and a model-based CF approach. Results show that probabilistic classifiers based on temporal behavior of users have better performance than traditional recommendation-based strategies, thus reflecting that temporal information is a valuable source for the identification or discrimination of user ratings.This work is supported by the Spanish Government (TIN 2008-06566-C04-02) and by the Comunidad de Madrid and Universidad Aut´onoma de Madrid (CCG10-UAM/TIC-5877). The authors acknowledge support from CCC at UAM

Towards a more realistic evaluation: Testing the ability to predict future tastes of matrix factorization-based recommenders

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '11 Proceedings of the fifth ACM conference on Recommender systems, http://dx.doi.org/10.1145/2043932.2043990.The use of temporal dynamic terms in Matrix Factorization (MF) models of recommendation have been proposed as a means to obtain better accuracy in rating prediction task. However, the way such models have been tested may not be a realistic setting for recommendation. In this paper, we evaluated rating prediction and top-N recommendation tasks using a MF model with and without temporal dynamic terms under two evaluation settings. Our experiments show that the addition of dynamic parameters do not necessarily yield to better results on these tasks when a more strict time-aware separation of train/test data is performed, and moreover, results may vary notably when different evaluation schemes are used.This work is supported by the Spanish Government (TIN 2008-06566-C04-02) and by the Comunidad de Madrid and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877)

Time feature selection for identifying active household members

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/2396761.2398628.Popular online rental services such as Netflix and MoviePilot often manage household accounts. A household account is usually shared by various users who live in the same house, but in general does not provide a mechanism by which current active users are identified, and thus leads to considerable difficulties for making effective personalized recommendations. The identification of the active household members, defined as the discrimination of the users from a given household who are interacting with a system (e.g. an on-demand video service), is thus an interesting challenge for the recommender systems research community. In this paper, we formulate the above task as a classification problem, and address it by means of global and local feature selection methods and classifiers that only exploit time features from past item consumption records. The results obtained from a series of experiments on a real dataset show that some of the proposed methods are able to select relevant time features, which allow simple classifiers to accurately identify active members of household accounts.This work was supported by the Spanish Government (TIN2011-28538-C02). The authors thank Centro de Computación Científica at UAM for its technical support

Time-aware evaluation of methods for identifying active household members in recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_3Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.Online services are usually accessed via household accounts. A household account is typically shared by various users who live in the same house. This represents a problem for providing personalized services, such as recommendation. Identifying the household members who are interacting with an online system (e.g. an on-demand video service) in a given moment, is thus an interesting challenge for the recommender systems research community. Previous work has shown that methods based on the analysis of temporal patterns of users are highly accurate in the above task when they use randomly sampled test data. However, such evaluation methodology may not properly deal with the evolution of the users’ preferences and behavior through time. In this paper we evaluate several methods’ performance using time-aware evaluation methodologies. Results from our experiments show that the discrimination power of different time features varies considerably, and moreover, the accuracy achieved by the methods can be heavily penalized when using a more realistic evaluation methodology

Movie recommendations based in explicit and implicit features extracted from the filmtipset dataset

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAMRa '10 Proceedings of the Workshop on Context-Aware Movie Recommendation, http://dx.doi.org/10.1145/1869652.1869660In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.This research was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Scientific Computing Institute at UAM. The third author also wants to acknowledge support from the Chilean Government through the Becas-Chile scholarship progra

Simple time-biased KNN-based recommendations

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAMRa '10 Proceedings of the Workshop on Context-Aware Movie Recommendation, http://dx.doi.org/10.1145/1869652.1869655.In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to weekly Filmtipset track. We consider simple strategies for taking into account the temporal context for movie recommendations, mainly based on variations of the KNN algorithm, which has been deeply studied in the literature, and one ad-hoc strategy, taking advantage of particular information in the weekly Filmtipset track. Results show that the usage of information near to the recommendation date alone can help improving recommendation results, with the additional benefit of reducing the information overload of the recommender engine. Furthermore, the use of social interaction information shows also a contribution in order to better predict a part of users' tastes.This research was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Scientific Computing Institute at UAM. The first author acknowledges support from the Chilean Government through the Becas-Chile scholarship progra

Coadministration of the Three Antigenic Leishmania infantum Poly (A) Binding Proteins as a DNA Vaccine Induces Protection against Leishmania major Infection in BALB/c Mice

Highly conserved intracellular proteins from Leishmania have been described as antigens in natural and experimental infected mammals. The present study aimed to evaluate the antigenicity and prophylactic properties of the Leishmania infantum Poly (A) binding proteins (LiPABPs). Three different members of the LiPABP family have been described. Recombinant tools based on these proteins were constructed: recombinant proteins and DNA vaccines. The three recombinant proteins were employed for coating ELISA plates. Sera from human and canine patients of visceral leishmaniasis and human patients of mucosal leishmaniasis recognized the three LiPABPs. In addition, the protective efficacy of a DNA vaccine based on the combination of the three Leishmania PABPs has been tested in a model of progressive murine leishmaniasis: BALB/c mice infected with Leishmania major. The induction of a Th1-like response against the LiPABP family by genetic vaccination was able to down-regulate the IL-10 predominant responses elicited by parasite LiPABPs after infection in this murine model. This modulation resulted in a partial protection against L. major infection. LiPABP vaccinated mice showed a reduction on the pathology that was accompanied by a decrease in parasite burdens, in antibody titers against Leishmania antigens and in the IL-4 and IL-10 parasite-specific mediated responses in comparison to control mice groups immunized with saline or with the non-recombinant plasmid. The results presented here demonstrate for the first time the prophylactic properties of a new family of Leishmania antigenic intracellular proteins, the LiPABPs. The redirection of the immune response elicited against the LiPABP family (from IL-10 towards IFN-γ mediated responses) by genetic vaccination was able to induce a partial protection against the development of the disease in a highly susceptible murine model of leishmaniasisThe study was supported in Spain by grants from Ministerio de Ciencia e Innovación FIS PI11/00095 and FISPI14/00366 from the Instituto de Salud Carlos III within the Network of TropicalDiseases Research (VI P I+D+I 2008-2011, ISCIII -Subdirección General de Redes y Centros de Investigación Cooperativa (RD12/0018/0009)). This work was also supported in Brazil by a grant from CNPq (Ciencia sem Fronteiras-PVE 300174/2014-4). A CBMSO institutional grant from Fundación Ramón Areces is also acknowledged. EAFC is a grant recipient of CNPq. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity