publication . Conference object . Preprint . Other literature type . Article . 2016

What does Matter for Top-k Publication Recommendation based on Twitter Profiles?

Ansgar Scherp;
Open Access
  • Published: 22 Jun 2016
Abstract
<p>So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine three different factors, namely profiling method, temporal decay, and richness of content. Regarding profiling, we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay functions, we apply sliding window and exponential decay. In terms of the richness of content, we compare recommendations using both full-texts and titles of publications and using only titles. Overall, the three factors ...
Subjects
free text keywords: recommender system, social media, user profiling, Computer Science - Digital Libraries, Computer Science - Information Retrieval, Uncategorized, zenodo, Sparse matrix, Profiling (computer programming), Data mining, computer.software_genre, computer, Information retrieval, Sliding window protocol, Topic model, Knowledge-based systems, Semantics, Computer science
Related Organizations
Funded by
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
Download fromView all 8 versions
ZENODO
Conference object . 2016
Provider: ZENODO
FigShare
Other literature type . 2016
Provider: FigShare
30 references, page 1 of 2

[1] D. M. Blei and J. D. La erty. Dynamic topic models. In ICML. ACM, 2006.

[2] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3, 2003.

[3] S. Bostandjiev, J. O'Donovan, and T. Ho&#x7f;llerer. TasteWeights: a visual interactive hybrid recommender system. In RecSys. ACM, 2012. [OpenAIRE]

[4] J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative ltering. In UAI. Morgan Kaufmann, 1998.

[5] J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi. Short and tweet: experiments on recommending content from information streams. In CHI. ACM, 2010. [OpenAIRE]

[6] W. Feng and J. Wang. We can learn your# hashtags: Connecting tweets to explicit topics. In ICDE. IEEE, 2014.

[7] F. Goossen, W. IJntema, F. Frasincar, F. Hogenboom, and U. Kaymak. News personalization using the CF-IDF semantic recommender. In WIMS. ACM, 2011. [OpenAIRE]

[8] T. L. Gri ths and M. Steyvers. Finding scienti c topics. NAS, 101, 2004. [OpenAIRE]

[9] T. J. Hazen. Direct and latent modeling techniques for computing spoken document similarity. In the Spoken Language Technology. IEEE, 2010.

[10] L. Hong and B. D. Davison. Empirical study of topic modeling in Twitter. In SOMA. ACM, 2010.

[11] D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender systems: an introduction. Cambridge University Press, 2010.

[12] P. Kapanipathi, P. Jain, C. Venkataramani, and A. Sheth. User interests identi cation on Twitter using a hierarchical knowledge base. In ESWC. Springer, 2014. [OpenAIRE]

[13] M. K. Khribi, M. Jemni, and O. Nasraoui. Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. In ICALT. IEEE, 2008. [OpenAIRE]

[14] J. Letierce, A. Passant, J. G. Breslin, and S. Decker. Understanding how twitter is used to spread scienti c messages. In WebSci. Web Science Trust, 2010.

[15] Y. Li, M. Yang, and Z. M. Zhang. Scienti c articles recommendation. In CIKM. ACM, 2013.

30 references, page 1 of 2
Related research
Abstract
<p>So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine three different factors, namely profiling method, temporal decay, and richness of content. Regarding profiling, we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay functions, we apply sliding window and exponential decay. In terms of the richness of content, we compare recommendations using both full-texts and titles of publications and using only titles. Overall, the three factors ...
Subjects
free text keywords: recommender system, social media, user profiling, Computer Science - Digital Libraries, Computer Science - Information Retrieval, Uncategorized, zenodo, Sparse matrix, Profiling (computer programming), Data mining, computer.software_genre, computer, Information retrieval, Sliding window protocol, Topic model, Knowledge-based systems, Semantics, Computer science
Related Organizations
Funded by
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
Download fromView all 8 versions
ZENODO
Conference object . 2016
Provider: ZENODO
FigShare
Other literature type . 2016
Provider: FigShare
30 references, page 1 of 2

[1] D. M. Blei and J. D. La erty. Dynamic topic models. In ICML. ACM, 2006.

[2] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3, 2003.

[3] S. Bostandjiev, J. O'Donovan, and T. Ho&#x7f;llerer. TasteWeights: a visual interactive hybrid recommender system. In RecSys. ACM, 2012. [OpenAIRE]

[4] J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative ltering. In UAI. Morgan Kaufmann, 1998.

[5] J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi. Short and tweet: experiments on recommending content from information streams. In CHI. ACM, 2010. [OpenAIRE]

[6] W. Feng and J. Wang. We can learn your# hashtags: Connecting tweets to explicit topics. In ICDE. IEEE, 2014.

[7] F. Goossen, W. IJntema, F. Frasincar, F. Hogenboom, and U. Kaymak. News personalization using the CF-IDF semantic recommender. In WIMS. ACM, 2011. [OpenAIRE]

[8] T. L. Gri ths and M. Steyvers. Finding scienti c topics. NAS, 101, 2004. [OpenAIRE]

[9] T. J. Hazen. Direct and latent modeling techniques for computing spoken document similarity. In the Spoken Language Technology. IEEE, 2010.

[10] L. Hong and B. D. Davison. Empirical study of topic modeling in Twitter. In SOMA. ACM, 2010.

[11] D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender systems: an introduction. Cambridge University Press, 2010.

[12] P. Kapanipathi, P. Jain, C. Venkataramani, and A. Sheth. User interests identi cation on Twitter using a hierarchical knowledge base. In ESWC. Springer, 2014. [OpenAIRE]

[13] M. K. Khribi, M. Jemni, and O. Nasraoui. Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. In ICALT. IEEE, 2008. [OpenAIRE]

[14] J. Letierce, A. Passant, J. G. Breslin, and S. Decker. Understanding how twitter is used to spread scienti c messages. In WebSci. Web Science Trust, 2010.

[15] Y. Li, M. Yang, and Z. M. Zhang. Scienti c articles recommendation. In CIKM. ACM, 2013.

30 references, page 1 of 2
Related research
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publication . Conference object . Preprint . Other literature type . Article . 2016

What does Matter for Top-k Publication Recommendation based on Twitter Profiles?

Ansgar Scherp;