Suchergebnisse

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.

Purpose
The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.


Design/methodology/approach
A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.


Findings
Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country's situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.


Research limitations/implications
The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.


Originality/value
This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

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).

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.

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/10.1145/1869652.1869660 ; 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 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 program

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 program

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13 ; Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013. ; Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain. ; This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542)

Acknowledgements This work has been financed by the Research Fund for Coal and Steel of the European Union (METHENERGY PLUS, contract 754077). Ukrit Chaemwinyoo thanks the Erasmus+ program of the European Union. ; Peer reviewed ; Publisher PDF