Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing

Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely

Group Recommendation with Temporal Affinities

International audienceWe examine the problem of recommending items to ad-hoc user groups. Group recommendation in collaborative rating datasets has received increased attention recently and has raised novel challenges. Different consensus functions that aggregate the ratings of group members with varying semantics ranging from least misery to pairwise disagreement, have been studied. In this paper, we explore a new dimension when computing group recommendations, that is, affinity between group members and its evolution over time. We extend existing group recommendation semantics to include temporal affinity in recommendations and design GRECA, an efficient algorithm that produces temporal affinity-aware recommendations for ad-hoc groups. We run extensive experiments that show substantial improvements in group recommendation quality when accounting for affinity while maintaining very good performance

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

Avaliação da relevância em grupos nas aplicações de redes sociais

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaA agregação de indivíduos em grupos corresponde a um padrão de comportamento típico na forma como são estabelecidas as conexões nos ambientes de redes sociais, onde existe uma grande quantidade e diversidade de informação produzida. Umas das formas de conseguir seleccionar a informação que é de interesse é ter em conta a organização dos utilizadores em grupos de indivíduos interrelacionados pelas suas características, interesses comuns e interacções. No entanto, devido à natureza dinâmica destes ambientes, existe uma necessidade constante de adaptação para que se assegure a utilidade dos grupos formados para os seus membros e se a informação partilhada se revela de utilidade para os utilizadores. Neste trabalho propõe-se uma abordagem para avaliação da relevância da informação produzida no contexto de grupos de utilizadores em redes sociais com vista a suportar uma avaliação da utilidade dos grupos existentes. Esta abordagem contempla as seguintes vertentes: informação produzida por cada utilizador para os seus grupos; caracterização e avaliação da relevância individual de cada utilizador no contexto do grupo; análise da relevância da informação partilhada pelos seus membros no contexto de um grupo. Como forma de validar a abordagem seguida foi desenvolvida uma ferramenta num contexto específico de uma plataforma de rede social (Facebook), ilustrando-se assim as diferentes vertentes mencionadas

Optimization-based User Group Management : Discovery, Analysis, Recommendation

User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. User data is a special type of data that is described by user demographics (e.g., age, gender, occupation, etc.) and user activities (e.g., rating, voting, watching a movie, etc.) The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. However, analysis tools for user data is still lacking.In this thesis, we believe there exists a unique opportunity to analyze user data in the form of user groups. This is in contrast with individual user analysis and also statistical analysis on the whole population. A group is defined as set of users whose members have either common demographics or common activities. Group-level analysis reduces the amount of sparsity and noise in data and leads to new insights. In this thesis, we propose a user group management framework consisting of following components: user group discovery, analysis and recommendation.The very first step in our framework is group discovery, i.e., given raw user data, obtain user groups by optimizing one or more quality dimensions. The second component (i.e., analysis) is necessary to tackle the problem of information overload: the output of a user group discovery step often contains millions of user groups. It is a tedious task for an analyst to skim over all produced groups. Thus we need analysis tools to provide valuable insights in this huge space of user groups. The final question in the framework is how to use the found groups. In this thesis, we investigate one of these applications, i.e., user group recommendation, by considering affinities between group members.All our contributions of the proposed framework are evaluated using an extensive set of experiments both for quality and performance.Les donn ́ees utilisateurs sont devenue de plus en plus disponibles dans plusieurs do- maines tels que les traces d'usage des smartphones et le Web social. Les donn ́ees util- isateurs, sont un type particulier de donn ́ees qui sont d ́ecrites par des informations socio-d ́emographiques (ex., ˆage, sexe, m ́etier, etc.) et leurs activit ́es (ex., donner un avis sur un restaurant, voter, critiquer un film, etc.). L'analyse des donn ́ees utilisa- teurs int ́eresse beaucoup les scientifiques qui travaillent sur les ́etudes de la population, le marketing en-ligne, les recommandations et l'analyse des donn ́ees `a grande ́echelle. Cependant, les outils d'analyse des donn ́ees utilisateurs sont encore tr`es limit ́es.Dans cette th`ese, nous exploitons cette opportunit ́e et proposons d'analyser les donn ́ees utilisateurs en formant des groupes d'utilisateurs. Cela diff`ere de l'analyse des util- isateurs individuels et aussi des analyses statistiques sur une population enti`ere. Un groupe utilisateur est d ́efini par un ensemble des utilisateurs dont les membres parta- gent des donn ́ees socio-d ́emographiques et ont des activit ́es en commun. L'analyse au niveau d'un groupe a pour objectif de mieux g ́erer les donn ́ees creuses et le bruit dans les donn ́ees. Dans cette th`ese, nous proposons un cadre de gestion de groupes d'utilisateurs qui contient les composantes suivantes: d ́ecouverte de groupes, analyse de groupes, et recommandation aux groupes.La premi`ere composante concerne la d ́ecouverte des groupes d'utilisateurs, c.- `a-d., compte tenu des donn ́ees utilisateurs brutes, obtenir les groupes d'utilisateurs en op- timisantuneouplusieursdimensionsdequalit ́e. Ledeuxi`emecomposant(c.-`a-d., l'analyse) est n ́ecessaire pour aborder le probl`eme de la surcharge de l'information: le r ́esultat d'une ́etape d ́ecouverte des groupes d'utilisateurs peut contenir des millions de groupes. C'est une tache fastidieuse pour un analyste `a ́ecumer tous les groupes trouv ́es. Nous proposons une approche interactive pour faciliter cette analyse. La question finale est comment utiliser les groupes trouv ́es. Dans cette th`ese, nous ́etudions une applica- tion particuli`ere qui est la recommandation aux groupes d'utilisateurs, en consid ́erant les affinit ́es entre les membres du groupe et son ́evolution dans le temps.Toutes nos contributions sont ́evalu ́ees au travers d'un grand nombre d'exp ́erimentations `a la fois pour tester la qualit ́e et la performance (le temps de r ́eponse)

Group Recommendations with Responsibility Constraints

Sosiaalisen median laajeneminen on johtanut siihen, että yhä useammin ihmiset muodostavat ryhmiä erilaisia aktiviteetteja varten, ja peräkkäisiä ryhmäsuositteluja tuottavat järjestelmät ovat nousseet suosituksi tutkimusalueeksi. Ryhmälle tehtävät suositukset ovat huomattavasti monimutkaisempia kuin yksittäiset suositukset, koska suosittelujärjestelmät joutuvat vastaamaan kaikkien ryhmän jäsenten usein ristiriitaisten etujen tasapainottamisesta. Ottaen huomioon suositusten vaikutus käyttäjien kokemaan järjestelmän suorituskykyyn (esim. elokuvasuositukset) ja suositustehtävien usein varsin arkaluontoinen luonne (esim. sähköisen terveydenhuollon suositukset), suositusten luomisprosessia tulee harkita huolellisesti. Näistä seikoista johtuen on tullut entistä tarpeellisemmaksi kehittää erilaisia vastuullisuusrajoitteita noudattavia suosituksia. Tällaisia vastuullisuusrajoitteita ovat muun muassa reiluus eli puolueettomuus, ja läpinäkyvyys , joka helpottaa järjestelmän prosessien ymmärtämistä. Jos näitä rajoituksia noudatetaan, niin ryhmäsuosittelijoista tulee monimutkaisempia. On edelleen haastavampaa, jos suosittelijat käsittelevät suositusten jonoa sen sijaan, että jokainen suositus käsitellään erillään muista. Intuitiivisesti järjestelmän tulee ottaa huomioon itsensä ja ryhmän välisen vuorovaikutuksen historia ja mukauttaa suosituksiaan aikaisempien suositusten vaikutuksen mukaisesti. Tämä havainto johtaa uuden suositusjärjestelmätyypin, peräkkäisten ryhmäsuositusjärjestelmien , syntymiseen. Tavalliset ryhmäsuositusmenetelmät ovat tehottomia, kun niitä käytetään peräkkäisessä skenaariossa. Ne tuottavat usein suosituksia, joita ei ole edes tarkoitettu reiluksi kaikkia ryhmän jäseniä kohtaan, eli kaikki ryhmän jäsenet eivät ole yhtä tyytyväisiä suosituksiin. Käytännössä, kun jokaista suositusprosessia tarkastellaan erikseen, aina löytyy vähiten tyytyväinen jäsen. Vähiten tyytyväisimmän jäsenen ei kuitenkaan pitäisi aina olla sama, kun järjestelmän käyttö kattaa useamman kuin yhden suosituskierroksen. Tämä johtaisi oikeudenmukaisuuden rajoitteen rikkomiseen, koska järjestelmä olisi puolueellinen yhtä ryhmän jäsentä vastaan. Suositusjärjestelmien monimutkaisuuden vuoksi käyttäjät eivät ehkä pysty ymmärtämään ehdotuksen perusteluja. Tämän torjumiseksi monet järjestelmät tarjoavat selityksiä ja suosituksia avoimuusrajoituksen mukaisesti. Keskustelu siitä, miksi kohdetta ei ehdoteta, on arvokasta erityisesti järjestelmänvalvojille. Selitykset tällaisiin kyselyihin ovat heille korvaamatonta palautetta, kun he ovat kalibroimassa tai korjaamassa järjestelmäänsä. Kaiken kaikkiaan tämän opinnäytetyön tavoitteena on vastata seuraaviin tutkimuskysymyksiin (RQ). RQ1. Kuinka määritellä peräkkäiset ryhmäsuositukset ja miksi niitä tarvitaan? Kuinka suunnitella ryhmäsuositusmenetelmiä niiden pohjalta? Tässä opinnäytetyössä määritellään formaalisti peräkkäinen ryhmäsuositusjärjestelmä ja mitä tavoitteita sen tulee noudattaa. Lisäksi ehdotetaan kolmea uutta ryhmäsuositusmenetelmää oikeudenmukaisten peräkkäisten ryhmäsuositusten tuottamiseksi. RQ2. Kuinka hyödyntää vahvistusoppimista ryhmäsuositusmenetelmän valinnassa, kun järjestelmän ympäristö muuttuu jokaisen suosituskierroksen jälkeen? RQ1:n laajennuksessa tässä opinnäytetyössä ehdotetaan vahvistukseen perustuvaa mallia, joka valitsee sopivimman ryhmäsuositusmenetelmän käytettäväksi koko sarjassa, samalla pyrkien reiluuteen. RQ3. Kuinka suunnitella kysymyksiä ja tuottaa selityksiä sille, miksi jokin joukko ei näkynyt suosituslistalla tai tietyssä paikassa? Tässä väitöskirjassa määritellään miksi-ei- kysymys ja esitetään näiden kysymysten rakenne. Lisäksi työssä ehdotetaan mallia, jolla luodaan selityksiä näihin miksi-ei-kysymyksiin. RQ4. Kuinka sisällyttää erilaisia terveyteen liittyviä näkökohtia ryhmäsuosituksiin? Näissä on tärkeää antaa oikeudenmukaisia suosituksia, koska terveyssuositukset ovat erittäin arkaluontoisia. Mahdollisimman oikeudenmukaisen suosituksen tuottamiseksi tässä opinnäytetyössä ehdotetaan mallia, joka sisältää erilaisia terveysnäkökohtia.The expansion of social media has led more people to form groups for specific activities, and, consecutively, group recommender systems have emerged as popular research. In contrast to single recommendations, group recommendations involve a much greater degree of complexity since the systems are responsible for balancing the often conflicting interests of all group members. Due to the impact of recommendations on users’ perceived performance (e.g., movie recommendations) and the often inherently sensitive nature of recommendation tasks (e.g., e-health recommendations), the process by which recommendations are generated should be carefully considered. As a result, it has become increasingly necessary to develop recommendations that adhere to various responsibility constraints. Such responsibility constraints include fairness , which corresponds to a lack of bias, and transparency , which facilitates an understanding of the processes of the system. Nevertheless, if these constraints are followed, group recommender systems be- come more complex. It is even more challenging if they are to consider a sequence of recommendations rather than each recommendation as a separate process. Intuitively, the system should take into account the historical interactions between itself and the group and adjust its recommendations in accordance with the impact of its previous suggestions. This observation leads to the emergence of a new type of recommender system, called sequential group recommendation systems. However, standard group recommendation approaches are ineffective when applied in a sequential scenario. They often produce recommendations that are not even intended to be fair to all group members, i.e., not all group members are equally satisfied with the recommendations. In practice, when each recommendation process is considered in isolation, there is always going to be a least satisfied member. However, the least satisfied member should not always be the same when the scope of the system encompasses more than one recommendation round. This will result in the fairness constraint being broken since the system is biased against one group member. As a result of the complex nature of recommender systems, users may be unable to understand the reasoning behind a suggestion. To counter this, many systems provide explanations along with their recommendations in adherence to the transparency constraint. Discussing why not suggesting an item is valuable, especially for system administrators. Explanations to such queries are invaluable feedback for them when they are in the process of calibrating or debugging their system. Overall, this thesis aims to answer the following Research Questions (RQ). RQ1. How to define sequential group recommendations, and why are they needed? How to de- sign group recommendation methods based on them? This thesis formally defines a sequential group recommender system and what objectives it should observe. Additionally, it proposes three novel group recommendation methods to produce fair sequential group recommendations. RQ2. How to exploit reinforcement learning to select a group recommendation method when the system’s environment changes after each recommendation round? In an extension of the RQ1, this thesis proposes a reinforcement-based model that selects the most appropriate group recommendation method to apply throughout a series of recommendations while aiming for fair recommendations. RQ3. How to design questions and produce explanations for why a set of items did not appear in a recommendation list or at a particular position? This dissertation defines what a Why-not question is, as well as presents a structure for them. Additionally, it proposes a model to generate explanations for these Why-not questions. RQ4. How to incorporate various health-related aspects in group recommendations? It is important to make fair recommendations when dealing with extremely sensitive health-related information. In order to produce as fair a recommendation as possible, this thesis proposes a model that incorporates various health aspects