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2018 | OriginalPaper | Buchkapitel

Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia

verfasst von : Giorgia Di Tommaso, Stefano Faralli, Giovanni Stilo, Paola Velardi

Erschienen in: The Semantic Web – ISWC 2018

Verlag: Springer International Publishing

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Abstract

This paper presents Wiki-MID, a LOD compliant multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. Our English dataset includes an average of 90 multi-domain preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million users traced during six months in 2017. Preferences are either extracted from messages of users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their “topical” friends, i.e., followees representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles describing them. This unique feature of our dataset provides a mean to categorize preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others.

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Vorheriges Kapitel Audio Commons Ontology: A Data Model for an Audio Content Ecosystem

Nächstes Kapitel HeLiS: An Ontology for Supporting Healthy Lifestyles

https://recsys.acm.org/wp-content/uploads/2014/10/recsys2014-tutorial-cross_domain.pdf.

https://recsys.acm.org/.

http://snap.stanford.edu/data.

https://www.kaggle.com/datasets/?sortBy=hottest&group=all.

https://developer.twitter.com/en/docs/api-reference-index.

We do not directly attempt a match of all \(f \in F\) with Wikipedia, since it is very computationally demanding and has a reduced precision.

As retrieved on January 2018.

The evaluation is rather straightforward, as readers may verify inspecting the released dataset and mappings.

https://www.kdnuggets.com/2016/02/nine-datasets-investigating-recommender-systems.html.

http://rdfs.org/sioc/spec/sioc.html.

http://www.w3.org/2004/02/skos/core.html.

Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef

Davidson, J., Liebald, B., Liu, J., et al.: The youtube video recommendation system. In: Proceedings of the 4th RecSys, pp. 293–296. ACM (2010)

Fouss, F., Saerens, M.: Evaluating performance of recommender systems: an experimental comparison. In: International Conference on WI-IAT 2008, vol. 1, pp. 735–738. IEEE (2008)

Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic approaches in recommendation systems. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 15–37. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_2CrossRef

Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9CrossRef

Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10CrossRef

Trewin, S.: Knowledge-based recommender systems. Encycl. Libr. Inf. Sci. 69(Suppl. 32), 180 (2000)

Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12(4), 331–370 (2002)CrossRefMATH

Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. JMLR 10, 2935–2962 (2009)MathSciNetMATH

10.

Dror, G., Koenigstein, N., Koren, Y., Weimer, M.: The yahoo! music dataset and KDD-cup’11. In: Proceedings of KDD Cup 2011, pp. 3–18 (2012)

11.

Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys 2008. ACM (2008)

12.

Kamishima, T., Akaho, S.: Nantonac collaborative filtering: a model-based approach. In: Proceedings of the 4th RecSys, pp. 273–276. ACM (2010)

13.

Sawant, S., Pai, G.: Yelp food recommendation system (2013)

14.

Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD, pp. 783–792 (2010)

15.

Mavalankar, A.A., et al.: Hotel recommendation system. Internal Report (2017)

16.

Çano, E., Morisio, M.: Characterization of public datasets for recommender systems. In: IEEE 1st International Forum on RTSI, pp. 249–257. IEEE (2015)

17.

Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. In: TiiS 2016 (2016)

18.

McFee, B., Bertin-Mahieux, T., Ellis, D.P., Lanckriet, G.R.: The million song dataset challenge. In: Proceedings of the 21st WWW, pp. 909–916. ACM (2012)

19.

Bennett, J., Lanning, S., et al.: The netflix prize. In: Proceedings of KDD, New York (2007)

20.

Yan, M., Sang, J., Xu, C.: Mining cross-network association for youtube video promotion. In: Proceedings of the 22nd ACM MM, pp. 557–566. ACM (2014)

21.

Piao, G., Breslin, J.G.: Inferring user interests in microblogging social networks: a survey. arXiv:1712.07691v3 (2017)

22.

Chaabane, A., Acs, G., Kaafar, M.A., et al.: You are what you like! information leakage through users’ interests. In: Proceedings of the 19th NDSS Symposium (2012)

23.

Faralli, S., Stilo, G., Velardi, P.: Large scale homophily analysis in Twitter using a twixonomy. In: Proceedings of 24th IJCAI, Buenos Aires, 25–31 July 2015, pp. 2334–2340 (2015)

24.

Piao, G., Breslin, J.G.: Inferring user interests for passive users on Twitter by leveraging followee biographies. In: Jose, J.M. (ed.) ECIR 2017. LNCS, vol. 10193, pp. 122–133. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_10CrossRef

25.

Pichl, M., Zangerle, E., Specht, G.: #Nowplaying on #Spotify: leveraging spotify information on Twitter for artist recommendations. Current Trends in Web Engineering. LNCS, vol. 9396, pp. 163–174. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24800-4_14CrossRef

26.

Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on Twitter using a hierarchical knowledge base. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 99–113. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_8CrossRef

27.

Schinas, E., et al.: Eventsense: capturing the pulse of large-scale events by mining social media streams. In: Proceedings of the 17th PCI, pp. 17–24. ACM (2013)

28.

Nichols, J., Mahmud, J., Drews, C.: Summarizing sporting events using Twitter. In: Proceedings of the 2012 International Conference on Intelligent User Interfaces, pp. 189–198. ACM (2012)

29.

Dooms, S., De Pessemier, T., Martens, L.: Mining cross-domain rating datasets from structured data on Twitter. In: Proceedings of the 23rd WWW, pp. 621–624. ACM (2014)

30.

Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: Proceedings of the 20th ACM SIGKDD, pp. 1266–1275. ACM (2014)

31.

Myers, S.A., Leskovec, J.: The bursty dynamics of the Twitter information network. In: Proceedings of the 23rd WWW, pp. 913–924. ACM (2014)

32.

Pichl, M., Zangerle, E., Specht, G.: Combining spotify and Twitter data for generating a recent and public dataset for music recommendation. In: Grundlagen von Datenbanken, pp. 35–40 (2014)

33.

Besel, C., Schlötterer, J., Granitzer, M.: Inferring semantic interest profiles from Twitter followees: does twitter know better than your friends? In: SAC 2016 (2016)

34.

Nechaev, Y., Corcoglioniti, F., Giuliano, C.: SocialLink: linking DBpedia entities to corresponding Twitter accounts. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 165–174. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_17CrossRef

35.

Faralli, S., Stilo, G., Velardi, P.: Automatic acquisition of a taxonomy of microblogs users’ interests. J. Web Semant. 45, 23–40 (2017)CrossRef

36.

Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. AI 193, 217–250 (2012)MathSciNetMATH

37.

Delli Bovi, L., Telesca, L., Navigli, R.: Large-scale information extraction from textual definitions through deep syntactic and semantic analysis. TACL 3, 529–543 (2015)CrossRef

Titel: Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia
verfasst von: Giorgia Di Tommaso
Stefano Faralli
Giovanni Stilo
Paola Velardi
Verlag: Springer International Publishing
Buch: The Semantic Web – ISWC 2018
Print ISBN: 978-3-030-00667-9

Electronic ISBN: 978-3-030-00668-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00668-6_3

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