A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Efektivní algoritmy pro problémy se sociálním vlivem u velkých sítí

In recent years, the dizzying explosion of data and information results from social networks with millions to billions of users, such as Facebook, YouTube, Twitter, and LinkedIn. Users can use online social networks (OSNs) to quickly trade information, communicate with other users, and keep their information up-to-date. The challenge of spreading information on social networks that arises in practice requires effective information management solutions, such as disseminating useful information, maximizing the influence of information transmission, and preventing disinformation, rumors, and viruses from being disseminated. Motivated by the above issues, we investigate the problem of information diffusion on OSNs. We study this problem based on two models, Independent Cascade (IC) and Linear Threshold (LT), and classical Influence Maximization (IM) in online social networks. In addition, we investigate various aspects of IM problems, such as budget variations, topics of interest, multiple competitors, and others. Moreover, we also investigate and apply the theory of combinatorial optimization problems to solve one of the current concerns in social networks, maximizing the influence on the groups and topics in social networks. In general, the main goals of the Ph.D thesis proposal are as follows. 1. We investigate the Multi-Threshold problem for IM, which is a variant of the IM problem with threshold constraints. We propose an efficient algorithm that IM for multiple thresholds in the social network. In particular, we develop a novel algorithmic framework that can use the solution to a smaller threshold to find that of larger ones. 2. We study the Group Influence Maximization problem and introduce an efficient group influence maximization algorithm with more advantages than each node’s influence in networks, using a novel sampling technique to estimate the epsilon group function. We also devised an approximation algorithm to estimate multiple candidate solutions with theoretical guarantee. 3. We investigate an approach for Influence Maximization problem with k-topic under constraints in social network. More specifically, we also study a streaming algorithm that combines an optimization algorithm to improve the approximation algorithm and theoretical guarantee in terms of solution quality and running time.V posledních letech je závratná exploze dat a informací výsledkem sociálních sítí s miliony až miliardami uživatelů, jako jsou Facebook, YouTube, Twitter a LinkedIn. Uživatelé mohou využívat online sociální sítě (OSNs) k rychlému obchodování s informacemi, komunikaci s ostatními uživateli a udržování jejich informací v aktuálním stavu. Výzva šíření informací na sociálních sítích, která se v praxi objevuje, vyžaduje efektivní řešení správy informací, jako je šíření užitečných informací, maximalizace vlivu přenosu informací a zabránění šíření dezinformací, fám a virů. Motivováni výše uvedenými problémy zkoumáme problém šíření informací na OSN. Tento problém studujeme na základě dvou modelů, Independent Cascade (IC) a Linear Threshold (LT) a klasické Influence Maximization (IM) v online sociálních sítích. Kromě toho zkoumáme různé aspekty problémů s rychlým zasíláním zpráv, jako jsou změny rozpočtu, témata zájmu, více konkurentů a další. Kromě toho také zkoumáme a aplikujeme teorii kombinatorických optimalizačních problémů k vyřešení jednoho ze současných problémů v sociálních sítích, maximalizujeme vliv na skupiny a témata v sociálních sítích. Obecně lze říci, že hlavní cíle Ph.D. návrh diplomové práce je následující. 1. Zkoumáme problém Multi-Threshold pro IM, což je varianta problému IM s prahovými omezeními. Navrhujeme účinný algoritmus, který IM pro více prahů v sociální síti. Zejména vyvíjíme nový algoritmický rámec, který může použít řešení pro menší práh k nalezení prahu většího. 2. Studujeme problém maximalizace vlivu skupiny a zavádíme účinný algoritmus maxima- lizace vlivu skupiny s více výhodami, než je vliv každého uzlu v sítích, pomocí nové vzorkovací techniky k odhadu funkce skupiny epsilon. Navrhujeme také aproximační algoritmus pro odhad více kandidátních řešení s teoretickou zárukou. 3. Zkoumáme přístup pro maximalizaci vlivu s k-téma pod omezeními v rozsáhlé síti. Konkrétněji budeme studovat novou metriku, která kombinuje optimalizační algoritmus pro zlepšení aproximačního algoritmu z hlediska kvality řešení a doby běhu na základě kliky a komunity v komplexních sítích.460 - Katedra informatikyvyhově

Multi-Modal Topic Sentiment Analytics for Twitter

Title from PDF of title page viewed February 1, 2019Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 50-53)Thesis (M.S.)--School of Computing and Engineering, University of Missouri--Kansas City, 2018Sentiment analysis has proven to be very successful in text applications. Social media is also considered a quite rich source to get data regarding user’s behaviors and preference. Identifying social context would make the sentiment analysis more meaningful to the applications. Due to the limited contextual information in social media, it would be quite challenging to conduct context-aware sentiment analysis with social media. Promising frameworks such as CoreNLP, Text Blob, and Vader have been introduced to identify sentiments in the text. However, it seems to not be adequate to contextual sentiment analysis in social media like Twitter. In this thesis, we present a contextual sentiment framework that is designed to leverage the power of the multiple models in the social context. The framework aims to classify contextual sentiment from the Twitter data as well as to discover hidden trends and topics (context) using topic modeling techniques like Latent Dirichlet Allocation (LDA). We have focused on the mismatch cases among multiple models in which different experts (models) have different opinions on social media sentiments. We have identified the five mismatch types in the social sentiment through the analysis of diverse experiments ( human machine model, and machine-machine model). We have implemented the mismatch detection among the three models (i.e., Vader, Text Blob, and CoreNLP) and automatically corrected them by applying semantic rules to sentiment models. We compared our approach against a traditional single model approach concerning a performance metric (accuracy) and Kappa (evaluating consensus among multi-models) on three benchmarks datasets and our dataset we collected from a health dieting domain. The proposed framework showed notable performance improvement in comparison with the traditional one concerning both evaluation metrics.Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future wor