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Autor
Strzelecki Artur (University of Economics in Katowice, Poland)
Tytuł
Website Content Recommendations Based on the Signals from Social Networks
Źródło
Scientific Publications / University of Economics in Katowice. Economics and Business Communication Challenges : International Week, 2014, s. 229-239, tab., bibliogr. 10 poz.
Słowa kluczowe
Media społecznościowe, Serwisy społecznościowe, Serwis internetowy
Social media, Social networking, Website
Uwagi
summ.
Abstrakt
The Internet, social media and network communities have been developed successfully for the last years. The implementation of the recommender systems is an obvious result of that development. The author argues that each product presented in Internet can be recommended as well as it can collect the recommendations signals. For that hypothesis's verification, author has gathered SEO and SEM data and he has analysed several websites. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Bibliografia
Pokaż
  1. Bosma N. (2014), Work with SEO in Excel - The Ultimate Excel Plugin, http://nielsbosma.se/projects/seotools/.
  2. Chen J., Naim R., Nelson L., Bernstein M., and Chi E. H. (2010), Short and Tweet: Experiments on Recommending Content from Information Streams, [in:] CHI '10: Proceedings of the 28th International Conference on Human Factors in Computing Systems, pp. 1185-1194.
  3. Freyne F., Jacovi M., Guy I., and Geyer W. (2009), Increasing Engagement through Early Recommender Intervention, [in:] RecSys '09: Proceedings of the Third ACM Conference on Recommender Systems, pp. 85-92.
  4. Gerlitz C, Helmond A. (2013), The Like Economy: Social Buttons and the Data-intensive Web, New Media & Society 2013: http://nms.sagepub.com/content/early/2013/02/03/ 1461444812472322, accessed October, 11,2014.
  5. Grabowicz P. A., Ramasco J. J., Moro E., Pujol J, M., and Eguiluz V. M. (2012), Social Features of Online Networks: The Strength of Intermediaiy Ties in Online Social Media, PloS one 7.1.
  6. Geyer W., Dugan C., Millen D. R., Muller M., and Freyne J. (2008), Recommending Topics for Self descriptions in Online User Profiles, [in:] Proceedings of the 2008 ACM Conference on Recommender Systems, New York, pp. 59-66.
  7. Guy I., Zwerdling N., Ronen I., Carmel D., and Uziel E. (2010), Social Media Recommendation based on People and Tags, [in:] Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, pp. 194-201.
  8. Hannon J., Bennett M., Smyth B. (2010), Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches, [in:] Proceedings of the Fourth ACM Conference on Recommender Systems, New York, pp. 199-206.
  9. Kwak H., Lee C., Park H., and Moon S. (2010), What is Twitter, a Social Network or a News Media? [in:] WWW '10: Proceedings of the 19th International Conference on World Wide Web, pp. 591-600.
  10. Page L., Brin S., Motwani R., Winograd T. (1998), The PageRank Citation Ranking: Bringing Order to the Web, Technical Report, Stanford University, Stanford.
Cytowane przez
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Język
eng
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