Information Outlook, October 2006

Volume 10, Issue 10https://scholarworks.sjsu.edu/sla_io_2006/1009/thumbnail.jp

Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision

Collaborative filtering in TV Recommender

The thesis describes different types of collaborative filtering methods to filter information from the large amount available and presents examples of such systems in different domains. It focuses on automated collaborative filtering to generate personalized recommendation of information. Different variations of the automated collaborative filtering scheme are developed and analyzed in the thesis. An additional adjustment of the predicted score is implemented in order to improve precision of the recommendation. Different combinations of parameters are analyzed to maximize system effectiveness. The data for the analysis was gathered through TV Recommender, a World Wide Web system developed for the thesis. The TV Recommender is a fully functional system that acquires users\u27 data and implements the enhanced collaborative filtering scheme to generate user\u27s personalized TV recommendation

Social support system in learning network for lifelong learners:a conceptual framework

Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in learning network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.Learning Networks are favorable model for supporting self-directed learning for lifelong learners. Learners can themselves decide about their learning plans to learn at their own pace irrespective of place and time. However, such learners remain hidden from others in the Learning Network., which makes their learning detrimental and less effective. Bringing learners together would benefit them in sharing each others expertise and learn effectively by collaboration. We propose to tackle the problem of finding people in learning networks by developing a Social Support System (SoSuSy) prototype. This position paper presents a conceptual framework for designing SoSuSy in a Learning Network. Such a system connects the learner with other learners who are dealing with similar problem by using their combined skills and to increase their social interaction. We propose by using people’s profile on social network and the public text content they create (blogs and book-marking) supported by web 2.0 applications, to enhance the search for finding suitable people who match in their interests, competence and tasks. We present an informal learning scenario to justify the need for such a system in online distributed Learning Network.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Hybrid Recommender for Online Petitions with Social Network and Psycholinguistic Features

The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple ranking mechanism adopted by most of the online petition platforms cannot effectively link most petitions with those who are really concerned with them. According to previous studies online, petitions seriousness has been questioned due to the rare chance of succeeding. At most, less than 10% of online petitions get the chance to fulfill their causes. To solve this problem, we present a design of a novel recommender system (PETREC). It leverages social interaction features, psycholinguistic features, and latent topic features to provide a personalized ranking to different users. Hence, it can give users better petition recommendations fitting their unique concerns. We evaluate PETREC against matrix factorization collaborative filtering and content-based filtering with the bag of words (Bow) features as two baseline recommenders for benchmarking. PETREC prediction performance outperformed Matrix factorization collaborative filtering, Bow petition-based content filtering, and Bow user-based content filtering with 4.2%, 1.7%, and 2.8% respectively as improvements in Root Mean Square Error (RMSE). The recommendation system described in this paper has potential to improve the user experience of online petition platforms. Thus, it is possible that it could encourage more public participation. Eventually, it will help the citizens to make a real difference through actively participating in online petitions that are matching their personalized concerns

WWW visibility in marketing

Abstract. Social media is a vital channel for marketers nowadays. Customers are more empowered today than ever before and the Internet is accelerating the trend toward greater customer empowerment. In few years Web 2.0 has become a highly important media and it has changed the Web into platform where individuals can communicate, assemble and organize data. Web 2.0 also offers a variety of different “tools” for companies to be used in marketing. Because companies and products are visible and discussed in social media, it is recommendable that companies try to seek positive publicity in these media. Thesis aims to describe the opportunities social media provides in organizational use, as well as, to provide an overview of the current situation in social media utilization in Finland. Further, it seeks to investigate the challenges organizations have in social media or in a whole field of E-marketing, and what kind of plans organizations have for the future in a field of E-marketing. The study consists of theoretical and empirical parts. Literature part scrutinizes the literature that covers different sides of online marketing. Empirical part of the study was conducted as a survey research. Results are based on a questionnaire and interviews that were conducted among Finnish companies during time period of spring and autumn 2012. The data was gathered among companies operating in different fields of business. Interviews were transcribed and conclusions were made from those. Because of the limited number of participants that took part to the questionnaire, the results derived from it are merely suggestive. Nevertheless, interviews did strengthen the understanding that was inherited from the questionnaire. The findings reveal that e-marketing has a very important role in the companies’ marketing strategy. Majority of the firms see social media marketing as a positive thing. Yet, the companies were unsure whether they possess the needed skills to do marketing in social media effectively. The results imply that the reason for this is related to the skills and experience the companies have in social media marketing. Those are such issues though, that company will learn and will develop its own way to use social media. This was also showed in the results.Tiivistelmä. Sosiaalinen media on tärkeä markkinointikanava nykypäivänä. Kuluttajilla on nykyään enemmän mahdollisuuksia vaikuttaa kuin koskaan ennen, minkä lisäksi Internet tarjoaa yhä kasvavassa määrin vaikuttamiskeinoja. Muutamassa vuodessa Web 2.0:sta on tullut erittäin tärkeä media, joka on muuttanut Internetin alustaksi, jossa ihmiset voivat kommunikoida sekä koota ja järjestää tietoa. Web 2. tarjoaa useita ”työkaluja” myös yrityksille käytettäväksi markkinoinnissa. Koska yritykset ja tuotteet ovat näkyvissä ja keskustelun kohteena sosiaalisissa medioissa, on suositeltavaa, että yritykset yrittävät hakea positiivista julkisuutta näissä medioissa. Tämä pro gradu -tutkielma pyrkii kuvailemaan mahdollisuuksia, joita sosiaalinen media tarjoaa organisaatioille, minkä lisäksi se tarjoaa yleiskuvan sosiaalisen median hyödyntämisestä Suomessa. Lisäksi tavoitteena on selvittää haasteita, joita organisaatioilla on sosiaalisessa mediassa tai laajemmin sähköisessä kaupankäynnissä, sekä selvittää yritysten tulevaisuuden visioita sähköiselle kaupankäynnille. Tämä työ koostuu kirjallisuuteen perustuvasta teoriaosuudesta sekä empiirisestä osuudesta. Kirjallisuusosuus tarjoaa laajan katsauksen tieteelliseen kirjallisuuteen verkkomarkkinoinnin eri puolilta. Työn empiiristä osuutta varten järjestettiin survey-tutkimus. Tulokset perustuvat kyselytutkimukseen ja haastatteluihin, jotka suoritettiin suomalaisten yritysten piirissä kevään ja syksyn 2012 välisenä aikana. Data kerättiin yrityksiltä, jotka edustivat eri toimialoja. Haastattelut litteroitiin ja päätelmät tehtiin niistä. Koska kyselyyn osallistuneiden yritysten määrää jäi rajalliseksi, kyselystä saadut tulokset ovat lähinnä suuntaa antavia. Siitä huolimatta, suoritetut haastattelut vain vahvistivat kuvaa, joka saatiin kyselystä. Tuloksista ilmenee, että sähköinen markkinointi on tärkeä osa yritysten markkinointistrategiaa. Suurin osa yrityksistä näkee sosiaalisessa mediassa tapahtuvan markkinoinnin positiivisena asiana. Kuitenkin yritykset olivat epävarmoja siltä, osaavatko he tehdä tehokasta markkinointia sosiaalisessa mediassa. Tulokset näyttävät, että epävarmuuden syy löytyy yritysten rajallisesta osaamisesta ja kokemuksesta sosiaalisen median markkinoinnin alueella. Nämä ovat tietenkin asioita, jotka yritys oppii ajan kuluessa ja joissa se kokemuksen karttuessa löytää oman, itselleen sopivan tavan toimia. Tämä tuli näytetyksi myös tuloksissa