Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification

Abstract—In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm’s practical value, we have first identified the self-supervised sentiment classification’s higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations ’ accuracy. Keywords-Information retrieval; sentiment analysis; opinion mining; online video recommendation. I

Understanding electronic word-of-mouth in tourism in the social media era

In recent decades, social media has fundamentally changed how communication takes place in business. It has contributed to the evolution of the Internet from a broadcasting medium to a participatory and interactive platform which allows users to generate and share information and become part of the media. For instance, social media has enabled the creation and exchange of electronic word-ofmouth (eWOM). We have witnessed the popularity of eWOM in travel and tourism industry. EWOM behaviour among individuals and the impact of eWOM on organizations have become important research focuses in eWOM research. However, the extant research has ignored the important function of social media platforms as both hedonic and social-oriented information systems (IS) for users, and few researchers have tried to explain eWOM use from the social, hedonic, and technology perspectives. eWOM application from the organizational perspective has also attracted the attention of researchers. Most prior studies in this field have focused primarily on the impact of eWOM on business performance and organizations’ eWOM strategy. However, the understanding of how social media platforms can be used to co-create value with customers and how eWOM can help organizations to engage customers is still fragmented. What is more, the understanding of the interplay between an organization’s activity and social media technology remains obscure. The objective of this study is threefold: 1) to explore eWOM use and generation behaviours among individuals by taking eWOM content, as well as the social media platform—that is, the channel of eWOM generation and use—into consideration; 2) to explore the value creation of social media and eWOM in organizations; and 3) to examine how social media and eWOM connect individuals and organizations and uncover the myth of how eWOM benefits both individuals and travel organizations. A combination of quantitative survey research and qualitative case study is used in this study. In particular, quantitative survey research method was used to explore the eWOM use among individual user’s to solve the research questions regarding the determinants of travellers’ eWOM use and generation behaviours. Qualitative case study method was used to solve the questions regarding how can tourism organizations use social media to co-create value with customers and to engage with customers via eWOM communication. This research includes empirical data collected from individual tourists in China and tourism organizations from both China and Finland. This study contributes to the understanding of eWOM in tourism context. Specifically, it contributes to the understanding of customers’ eWOM behavior by taking the social and hedonic functions into consideration, and sheds light on the understanding of eWOM application in organizations. This study also integrates eWOM research from both individual and organization perspectives and helps to explain the eWOM interplay between them. From practical view, the results of this study have important implications for tourism e-service practitioners in their understanding of customers’ decision making process, and the strategy to facilitate customers’ propensity of eWOM generation behavior. It also helps eWOM website designers to make successful eWOM websites. The findings also shed lights on e-service providers on how to co-create value with customers via social media platform and how to engage customer via eWOM communication.Sosiaalinen media on ratkaisevasti muuttanut tapaa jolla liikeviestintää hoidetaan nykyvuosikymmenellä. Se on muuttanut Internetin yksisuuntaisesta julkaisukanavasta yhteisölliseksi ja interaktiiviseksi alustaksi joka mahdollistaa käyttäjien informaation tuottamisen ja jakamisen, heidän tulemisensa osaksi mediaa. Esimerkiksi, sosiaalinen media on mahdollistanut eWOM-ilmiön, jolla tarkoitetaan asiakkaiden ja käyttäjien jostain kohteesta tekemien arvioiden antamista ja jakamista sähköisillä alustoilla. eWOM on osoittautunut erittäin suosituksi matkailun ja turismin alalla viime vuosina. eWOM-yksilökäyttäytyminen sekä se miten eWOM vaikuttaa organisaatioihin ovat tulleet tärkeiksi tutkimuskohteiksi eWOM-tutkimuksessa. Kuitenkin nykyinen tutkimus on jättänyt ottamatta huomioon sen että sosiaalisen median alustat toimivat myös mielihyvää tuottavina ja sosiaalisesti orientoituneina tietojärjestelminä käyttäjille, ja vain vähäinen tutkimus on yrittänyt selvittää eWOMkäyttöä sosiaalisesta, hedonisesta ja teknologisesta näkökulmasta. eWOM tutkimus organisatorisesta näkökulmasta keskittyy pääosin siihen miten eWOM vaikuttaa liiketoimintaan ja organisaation eWOM-strategiaan. Kuitenkin sen ymmärtäminen miten sosiaalisen median alustoja voidaan käyttää arvon yhteiseen tuottamiseen ja kuinka eWOM voi auttaa organisaatioita sitouttamaan asiakkaan on yhä sirpaleista. Lisäksi, organisaation toiminnan ja sosiaalisen median teknologian vuorovaikutuksen ymmärtäminen on yhä häilyvää. Tällä tutkimuksella on kolme tavoitetta: 1) tutkia eWOM-käyttöä ja tiedon tuottamista yksilötasolla ottamalla eWOM-sisältö samoin kuin sosiaalisen median alusta – kanava eWOMin tuottamiseen ja jakamiseen – huomioon; 2) tutkia eWOMin sosiaalisen median arvontuottoa organisaatiossa; 3) tutkia miten sosiaalinen media ja eWOM yhdistävät yksilöitä ja organisaatioita, jotta voitaisiin selvittää myytti siitä miten eWOM hyödyntää sekä yksilöitä että organisaatioita. Tässä tutkimuksessa käytettiin kvantitatiivisen kyselytutkimuksen ja kvalitatiivisen tapaustutkimuksen yhdistelmää. Erityisesti, kvantitatiivista kyselytutkimusta käytettiin tutkimusmenetelmänä tutkittaessa yksilöiden eWOM-käyttöä, jotta voitiin vastata tutkimuskysymykseen koskien matkustajien eWOMgeneroinnin ja –käytön keskeisiä suureita. Laadullista tapaustutkimusta käytettiin tutkimusmenetelmänä selvitettäessä sitä miten organisaatiot käyttävät sosiaalista mediaa tuottaakseen lisäarvoa asiakkaiden kanssa ja miten ne sitouttavat asiakkaat eWOM-viestinnän keinoin. Tämä tutkimus sisältää empiiristä dataa jota on kerätty sekä yksittäisiltä matkustajilta Kiinassa sekä matkailualan yrityksiltä sekä Kiinassa että Suomessa. Tämä tutkimus lisää ymmärrystä eWOM-ilmiöstä matkailussa. Erityisesti se tuottaa tietoa asiakkaiden eWOM-käyttäytymisestä ottamalla eWOMin sosiaaliset ja hedoniset aspektit huomioon, ja tuottaa tietoa eWOMin käytön ymmärtämiseksi organisaatioissa. Tämä tutkimus integroi eWOM-tutkimuksen sekä yksilön että organisaation näkökulmasta ja auttaa ymmärtämään näkökulmien yhteydet. Käytännön näkökulmasta tämän tutkimuksen tuloksilla on tärkeitä viestejä sähköisen palvelutuotannon toteuttajille kun he pyrkivät ymmärtämään asiakkaan päätöksentekoa. Se myös auttaa eWOM-suunnittelijoita tekemään onnistuneita eWOM-verkkopalveluita. Tutkimustulokset myös kertovat verkkopalveluiden tuottajille miten tuottaa lisäarvoa yhdessä asiakkaiden kanssa sosiaalisessa mediassa ja miten sitouttaa asiakkaat eWOM-kommunikaation avulla

Horizon Report 2009

El informe anual Horizon investiga, identifica y clasifica las tecnologías emergentes que los expertos que lo elaboran prevén tendrán un impacto en la enseñanza aprendizaje, la investigación y la producción creativa en el contexto educativo de la enseñanza superior. También estudia las tendencias clave que permiten prever el uso que se hará de las mismas y los retos que ellos suponen para las aulas. Cada edición identifica seis tecnologías o prácticas. Dos cuyo uso se prevé emergerá en un futuro inmediato (un año o menos) dos que emergerán a medio plazo (en dos o tres años) y dos previstas a más largo plazo (5 años)

Semantically-enhanced recommendations in cultural heritage

In the Web 2.0 environment, institutes and organizations are starting to open up their previously isolated and heterogeneous collections in order to provide visitors with maximal access. Semantic Web technologies act as instrumental in integrating these rich collections of metadata by defining ontologies which accommodate different representation schemata and inconsistent naming conventions over the various vocabularies. Facing the large amount of metadata with complex semantic structures, it is becoming more and more important to support visitors with a proper selection and presentation of information. In this context, the Dutch Science Foundation (NWO) funded the Cultural Heritage Information Personalization (CHIP) project in early 2005, as part of the Continuous Access to Cultural Heritage (CATCH) program in the Netherlands. It is a collaborative project between the Rijksmuseum Amsterdam, the Eindhoven University of Technology and the Telematica Instituut. The problem statement that guides the research of this thesis is as follows: Can we support visitors with personalized access to semantically-enriched collections? To study this question, we chose cultural heritage (museums) as an application domain, and the semantically rich background knowledge about the museum collection provides a basis to our research. On top of it, we deployed user modeling and recommendation technologies in order to provide personalized services for museum visitors. Our main contributions are: (i) we developed an interactive rating dialog of artworks and art concepts for a quick instantiation of the CHIP user model, which is built as a specialization of FOAF and mapped to an existing event model ontology SEM; (ii) we proposed a hybrid recommendation algorithm, combining both explicit and implicit relations from the semantic structure of the collection. On the presentation level, we developed three tools for end-users: Art Recommender, Tour Wizard and Mobile Tour Guide. Following a user-centered design cycle, we performed a series of evaluations with museum visitors to test the effectiveness of recommendations using the rating dialog, different ways to build an optimal user model and the prediction accuracy of the hybrid algorithm. Chapter 1 introduces the research questions, our approaches and the outline of this thesis. Chapter 2 gives an overview of our work at the first stage. It includes (i) the semantic enrichment of the Rijksmuseum collection, which is mapped to three Getty vocabularies (ULAN, AAT, TGN) and the Iconclass thesaurus; (ii) the minimal user model ontology defined as a specialization of FOAF, which only stores user ratings at that time, (iii) the first implementation of the content-based recommendation algorithm in our first tool, the CHIP Art Recommender. Chapter 3 presents two other tools: Tour Wizard and Mobile Tour Guide. Based on the user's ratings, the Web-based Tour Wizard recommends museum tours consisting of recommended artworks that are currently available for museum exhibitions. The Mobile Tour Guide converts recommended tours to mobile devices (e.g. PDA) that can be used in the physical museum space. To connect users' various interactions with these tools, we made a conversion of the online user model stored in RDF into XML format which the mobile guide can parse, and in this way we keep the online and on-site user models dynamically synchronized. Chapter 4 presents the second generation of the Mobile Tour Guide with a real time routing system on different mobile devices (e.g. iPod). Compared with the first generation, it can adapt museum tours based on the user's ratings artworks and concepts, her/his current location in the physical museum and the coordinates of the artworks and rooms in the museum. In addition, we mapped the CHIP user model to an existing event model ontology SEM. Besides ratings, it can store additional user activities, such as following a tour and viewing artworks. Chapter 5 identifies a number of semantic relations within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). We applied all these relations as well as the basic artwork features in content-based recommendations and compared all of them in terms of usefulness. This investigation also enables us to look at the combined use of artwork features and semantic relations in sequence and derive user navigation patterns. Chapter 6 defines the task of personalized recommendations and decomposes the task into a number of inference steps for ontology-based recommender systems, from a perspective of knowledge engineering. We proposed a hybrid approach combining both explicit and implicit recommendations. The explicit relations include artworks features and semantic relations with preliminary weights which are derived from the evaluation in Chapter 5. The implicit relations are built between art concepts based on instance-based ontology matching. Chapter 7 gives an example of reusing user interaction data generated by one application into another one for providing cross-application recommendations. In this example, user tagging about cultural events, gathered by iCITY, is used to enrich the user model for generating content-based recommendations in the CHIP Art Recommender. To realize full tagging interoperability, we investigated the problems that arise in mapping user tags to domain ontologies, and proposed additional mechanisms, such as the use of SKOS matching operators to deal with the possible mis-alignment of tags and domain-specific ontologies. We summarized to what extent the problem statement and each of the research questions are answered in Chapter 8. We also discussed a number of limitations in our research and looked ahead at what may follow as future work

A Matrix Framework Factorization on a Sentiment Based Rating Prediction Method tackles Cyber bullying Detection

It displays a great chance to share our perspectives for different items we buy. In any case, we confront the data over-overloading issue. Instructions to mine profitable data from audits to comprehend a client's inclinations and make an exact proposal is critical. Customary recommender systems (RS) think of some as variables, for example, client's buy records, item classification, and geographic area. In this work, we propose a supposition based rating prediction technique (RPS) to enhance expectation exactness in recommender systems. In this paper, we extricate item highlights from literary audits utilizing LDA. We for the most part need to get the item highlights including some named elements and some item/thing/benefit characteristics. LDA is a Bayesian model, which is used to show the relationship of audits, points and words.

Framework of Matrix Factorization to Achieve Rating Prediction Task

We propose a social client wistful estimation approach and figure every client's notion on things/items. Besides, we consider a client's own wistful properties as well as contemplate relational nostalgic impact. At that point, we consider item notoriety, which can be induced by the sentimental distributions of a client set that mirror clients' exhaustive assessment. Finally, we intertwine three components client sentiment likeness, relational nostalgic impact, and thing's notoriety closeness into our recommender framework to make a precise rating prediction. We lead an execution assessment of the three nostalgic components on a genuine dataset gathered from Yelp

Artificial Intelligence and Education. Guidance for Policy-makers

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts

Building Brand Reputation in the Digital Age: Identifying effective brand communication to win the moment of truth online

Thesis purpose: The central purpose is to deliver a theoretical and practical contribution to existing literature in the fields of brand identity as well as brand reputation and particularly brand communication as a connecting link, lying in between. Further, the authors attempt to provide thorough understanding of the influence of online brand communication on consumers’ decision-making process respectively the critical moment of truth online. In this context, a newly created brand management model is introduced. Theoretical perspective: The literature review covers the interconnectedness between brand identity and brand reputation and theoretically examines the consumer decision-making journey in an online context. It creates the basis for the subsequent empirical research. Thereby existing brand identity frameworks have been reviewed in detail. Methodology: The authors apply a grounded theory strategy. Thereby a mixed method approach is used by combining both qualitative and quantitative data collection. Empirical Data: Empirical data is gathered through in-depth interviews with twelve technology affine consumers. Subsequently, quantitative data is collected through an online survey in order to further evaluate the qualitative findings. Conclusion: The authors conclude that multiple online communication channels have an impact on the creation of positive brand reputation and consequently a consumer’s decision making process. Thereby valuable guidance to the management process of online brand communication in order to establish positive brand reputation is provided. This is presented through a newly created model- The Brand Identity Communication Reputation Matrix (BICRM), which builds upon existing theory in the fields of brand reputation