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

Physical activity in England: Who is meeting the recommended level of participation through sports and exercise?

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2012 Anokye et al.Background: Little is known about the correlates of meeting recommended levels of participation in physical activity (PA) and how this understanding informs public health policies on behaviour change. Objective: To analyse who meets the recommended level of participation in PA in males and females separately by applying ‘process’ modelling frameworks (single vs. sequential 2-step process). Methods: Using the Health Survey for England 2006, (n = 14 142; ≥16 years), gender-specific regression models were estimated using bivariate probit with selectivity correction and single probit models. A ‘sequential, 2-step process’ modelled participation and meeting the recommended level separately, whereas the ‘single process’ considered both participation and level together. Results: In females, meeting the recommended level was associated with degree holders [Marginal effect (ME) = 0.013] and age (ME = −0.001), whereas in males, age was a significant correlate (ME = −0.003 to −0.004). The order of importance of correlates was similar across genders, with ethnicity being the most important correlate in both males (ME = −0.060) and females (ME = −0.133). In females, the ‘sequential, 2-step process’ performed better (ρ = −0.364, P < 0.001) than that in males (ρ = 0.154). Conclusion: The degree to which people undertake the recommended level of PA through vigorous activity varies between males and females, and the process that best predicts such decisions, i.e. whether it is a sequential, 2-step process or a single-step choice, is also different for males and females. Understanding this should help to identify subgroups that are less likely to meet the recommended level of PA (and hence more likely to benefit from any PA promotion intervention).This study was funded by the Department of Health’s Policy Research Programme

Online television library: organization and content browsing for general users

This paper describes the organisational and playback features of Físchlár, a digital video library that allows users to record, browse and watch television programmes online. Programmes that can be watched and recorded are organised by personal recommendations, genre classifications, name and other attributes for access by general television users. Motivations and interactions of users with online television libraries are outlined and they are also supported by personalised library access, categorised programmes, a combined player browser with content viewing history and content marks. The combined player browser supports a user who watches a programme on different occasions in a non-sequential order

Rating scale development: a multistage exploratory sequential design

The project chosen to showcase the application of the exploratory sequential design in second/ foreign (L2) language assessment comes from the context of rating scale development and focuses on the development of a set of scales for a suite of high-stakes L2 speaking tests. The assessment of speaking requires assigning scores to a speech sample in a systematic fashion by focusing on explicitly defined criteria which describe different levels of performance (Ginther 2013). Rating scales are the instruments used in this evaluation process, and they can be either holistic (i.e. providing a global overall assessment) or analytic (i.e. providing an independent evaluations for a number of assessment criteria, e.g. Grammar, Vocabulary, Organisation, etc.). The discussion in this chapter is framed within the context of rating scales in speaking assessment. However, it is worth noting that the principles espoused, stages employed and decisions taken during the development process have wider applicability to performance assessment in general

Assessment of proofreading and editing with technical diploma students at Western Wisconsin Technical College - Mauston

Plan BProofreading and editing are a major component of the Office Assistant program. The practices of proofreading and editing are an integral part of primary skills employers expect from their employees. The ability to proofread and edit a document are critical components in reading and writing skills that employers look for in hiring people or in choosing an employee for promotion. The purpose of this study was to determine the degree of how proofreading and editing help students perceive themselves as better writers as they progress through the process of proofreading, editing, journal writing, error logs and peer editing. Nine students, who entered the Technical Diploma Office Assistant program at Western Wisconsin Technical College - Mauston campus, in August, 1999 and graduated in May 2000, comprised the samples. A proofreading and editing pretest was administered to the entering Technical Diploma class in September of 1999 - prior to the beginning of program instruction. Proofreading and editing assignments were given in September 1999, October 1999 and November 1999. A posttest was given in November of 1999. The researcher at Western Wisconsin Technical College - Mauston campus, administered the pretest, assignments and posttest

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

An investigation of first grade elementary teacher candidates’ perceptions of their teaching profession competencies: A mixed method study

Although teaching profession has lost its prestige over the years, the recent studies demonstrate that teaching still ranks among the top of most respected of professions. Teacher candidates’ individual characteristics play a crucial role in choosing teachers and designing curriculum for educating teacher candidates. The present study is of vital importance to identify the perceptions of first grade teacher candidates enrolled in Faculty of Education towards teaching profession and whether they have teaching competencies needed or not. In this research, a mixed research, sequential explanatory mixed design was utilized in which both quantitative and qualitative data were analyzed together. The quantitative data were collected through using teaching competency scale for teacher candidates and afterwards quantitative data was statistically analyzed. It was thus attempted to draw a general picture of the research problem. According to the results obtained, an in-depth analysis of teacher candidates’ perceptions of teaching competencies was required. Employing quantitative data, purposive sampling was identified and semi-structured interviews were carried out with the participants in the purposive sampling. The unidimensional scale developed by [1] and adapted into Turkish by [2] was employed for data collection in the quantitative stage of the research. When viewing teacher candidates’ perceptions on their teaching competencies, first grade teacher candidates stated that they perceive themselves competent in terms of attitude and values and vocation skills. © 2018 by authors, all rights reserved