Mining and Representing User Interests: The Case of Tagging Practices

Kim, Haklae; Breslin, John G.; Decker, Stefan; Kim, Hong-Gee

doi:10.1109/tsmca.2011.2132709

Cited by 14 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) to improve estimation accuracy considering 1) simple but more sensors such as not high precision but handy EEG [34] and 2) misunderstandings due to various situations such as those caused by global contexts; 2) to further improve estimation accuracy using 1) active sensing such as external stimulus [60], [61] but with the elaboration of less disturbance to user tasks and 2) reinforcement learning type information requests [62]; 3) to improve adaptive service capability by introducing more personalized text summarization and keyword/topic multiphase extraction functions [63], [64].…”

Section: Discussionmentioning

confidence: 99%

Enriched Cyberspace Through Adaptive Multimedia Utilization for Dependable Remote Collaboration

Takada

Sakurai

Kinshuk

et al. 2012

IEEE Trans. Syst., Man, Cybern. A

View full text Add to dashboard Cite

Due to the geographical distribution, different cognitive capacity, and different domain competency of workers or learners, many misunderstandings can occur during distributed remote collaboration, leading to inefficient discussions and undesired results. To make remote collaboration more efficient and dependable, enriching cyberspace through adaptively utilizing multimedia information is proposed and evaluated. This assesses situations of remote users through information fusion of multiple biomedical sensors and the related contexts such as user profiles. Transmitting and using such information, the system adaptively supports the distributed remote collaboration by stressing, warning, and presenting keywords/summaries in multimedia. Effects of presenting keywords/summaries adaptively depending on situations and cognitive profiles of remote members are evaluated as to the decrease of not-/misunderstanding possibilities during the explanation on the Cyberspace. The evaluation demonstrates the feasibility and usefulness of the proposed method.

show abstract

Section: Discussionmentioning

confidence: 99%

Enriched Cyberspace Through Adaptive Multimedia Utilization for Dependable Remote Collaboration

Takada

Sakurai

Kinshuk

et al. 2012

IEEE Trans. Syst., Man, Cybern. A

View full text Add to dashboard Cite

show abstract

“…Such platforms provide potential consumers with valuable information, from which they can make more efficient and rational purchase decisions (Cao et al 2011). For manufacturers and brokers, customer reviews allow them to more thoroughly understand their products, as well as market trends and consumer preferences (Jiang et al 2010;Kim et al 2011;Ku et al 2006;. Moreover, customer reviews positively affect both consumer demand (Mudambi and Schuff 2010) and sales revenues (Chevalier and Mayzlin 2006).…”

Section: Customer Reviews and Sentiment Analysismentioning

confidence: 99%

Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews

Pham

Chuang

et al. 2015

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

Online review forums provide customers with powerful platforms to express opinions and influence business trends, while allowing firms to collaborate and co-create value with customers. However, information overload due to the huge amount of reviews posted daily complicates the efforts of consumers to locate reliable information when making a purchase decision. Therefore, this study develops a trustworthy co-created recommendation model. The proposed model mines unboxing reviews, calculates the trust scores of the reviewers, and then generates the recommended products by combing this information with customer preferences using a multi-criteria decision-making method. An illustrative example of mobile phones demonstrates the recommendation procedure of the proposed model. The proposed model is evaluated via an empirical experiment to examine the satisfaction of study participants by using a seven-point Likert scale. An analysis of the structural equation modelling results indicates that three factors (i.e. confidence in decision quality, enhanced problem-solving ability, and satisfaction with resource expenditure) significantly and positively affect the purchase decision-making process. Moreover, the proposed model outperforms a baseline model in all four factors, ultimately increasing user satisfaction. In addition to its theoretical framework for co-creating value with customers to develop a trustworthy co-created recommendation model, as supported by various theories of trust, the proposed model provides further insights into the role of customer reviews in designing recommendation models, as well as the extent to which such models impact user decisions.

show abstract

“…and Representing User Interests using FCA [10] Tags are used to depict people's interest in online resources, and tags in most social Web sites have become an available feature. Since tags are used to organize individuals' ideas and thoughts as well as to encourage their social interactions, tagging activities on social web sites can be considered a new way of collective authorship.…”

Section: Miningmentioning

confidence: 99%

A Survey on Decision Support Systems in Social Media

Thangaraj¹,

Indra²

2015

IJCATR

View full text Add to dashboard Cite

Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about "feeding you the information that you want, when you want it" i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.

show abstract

Mining and Representing User Interests: The Case of Tagging Practices

Cited by 14 publications

References 35 publications

Enriched Cyberspace Through Adaptive Multimedia Utilization for Dependable Remote Collaboration

Enriched Cyberspace Through Adaptive Multimedia Utilization for Dependable Remote Collaboration

Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews

A Survey on Decision Support Systems in Social Media

Contact Info

Product

Resources

About