TagRec - Revision history

Alan at 11:19, 31 July 2015

2015-07-31T11:19:38Z

Alan at 11:18, 31 July 2015

2015-07-31T11:18:21Z

Alan at 11:17, 31 July 2015

2015-07-31T11:17:31Z

Ctrattner: /* Towards A Standardized Tag Recommender Benchmarking Framework */

2015-03-27T17:02:19Z

Towards A Standardized Tag Recommender Benchmarking Framework

Ctrattner at 12:04, 27 March 2015

2015-03-27T12:04:13Z

Ctrattner: /* External links */

2015-03-27T12:03:28Z

External links

Ctrattner: Created page with "== Towards A Standardized Tag Recommender Benchmarking Framework == ''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.p..."

2015-03-27T12:00:19Z

Created page with "== Towards A Standardized Tag Recommender Benchmarking Framework == ''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.p..."

New page

== Towards A Standardized Tag Recommender Benchmarking Framework ==

''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster

The aim of '''TagRec''' is to provide the community with a simple to use, '''generic tag-recommender framework''' written in '''Java''' to evaluate novel tag-recommender algorithms with a set of well-known std. IR metrics such as nDCG, MAP, MRR, Precision (P@k), Recall (R@k), F1-score (F1@k), Diversity (D), Serendipity (S), User Coverage (UC) and folksonomy datasets such as BibSonomy, CiteULike, LastFM, Flickr, MovieLens or Delicious and to benchmark the developed approaches against state-of-the-art tag-recommender algorithms such as MP, MP_r, MP_u, MP_u,r, CF, APR, FR, GIRP, GIRPTM, etc.

Furthermore, it contains algorithms to process datasets (e.g., p-core pruning, leave-one-out or 80/20 splitting, LDA topic creation and create input files for other recommender frameworks).

The software already '''contains four novel tag-recommender approaches based on cognitive science theory'''. The first one (3Layers) (Seitlinger et al, 2013) uses topic information and is based on the ALCOVE/MINERVA2 theories (Krutschke, 1992; Hintzman, 1984). The second one (BLL+C) (Kowald et al., 2014b) uses time information is based on the ACT-R theory (Anderson et al., 2004). The third one (3LT) (Kowald et al., 2015b) is a combination of the former two approaches and integrates the time component on the level of tags and topics. Finally, the fourth one (BLLac+MPr) extends the BLL+C algorithm with semantic correlations (Kowald et al., 2015a).

Based on our latest strand of research, '''TagRec also contains algorithms for the personalized recommendation of items''' in social tagging systems. In this respect TagRec includes a novel algorithm called CIRTT (Lacic et al., 2014) that integrates tag and time information using the BLL-equation coming from the ACT-R theory (Anderson et al, 2004). Furthermore, it contains another novel item-recommender called SUSTAIN+CFu (Seitlinger et al., 2015) that improves user-based CF via integrating the addentional focus of users via the SUSTAIN model (Love et al., 2004).
RiVal specifically focuses on transparency and reproducibility.

== Citation ==
* C. Trattner, D. Kowald and E. Lacic: [http://www.christophtrattner.info/pubs/sigweb2015.pdf TagRec: Towards a Toolkit for Reproducible Evaluation and Development of Tag-Based Recommender Algorithms], ACM SIGWEB Newsletter, Spring 2015, ACM, New York, NY, USA, 2015.

Bibtex: @article{Trattner:2015:TTT:2719943.2719946, author = {Trattner, Christoph and Kowald, Dominik and Lacic, Emanuel}, title = {TagRec: Towards a Toolkit for Reproducible Evaluation and Development of Tag-based Recommender Algorithms}, journal = {SIGWEB Newsl.}, issue_date = {Winter 2015}, year = {2015}, pages = {3:1--3:10}, numpages = {10}, publisher = {ACM}, address = {New York, NY, USA}, }

* D. Kowald, E. Lacic, and C. Trattner. [http://www.christophtrattner.info/pubs/ht241-kowald.pdf Tagrec: Towards a standardized tag recommender benchmarking framework]. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT'14, New York, NY, USA, 2014. ACM.

Bibtex: @inproceedings{Kowald2014TagRec, author = {Kowald, Dominik and Lacic, Emanuel and Trattner, Christoph}, title = {TagRec: Towards A Standardized Tag Recommender Benchmarking Framework}, booktitle = {Proceedings of the 25th ACM Conference on Hypertext and Social Media}, series = {HT '14}, year = {2014}, isbn = {978-1-4503-2263-8}, location = {Santiago de Chile, Chile}, publisher = {ACM}, address = {New York, NY, USA}, }

== Literature ==
* P. Seitlinger, D. Kowald, S. Kopeinik, I. Hasani-Mavriqi, T. Ley, and Elisabeth Lex: Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics. Under review. 2015.
* D. Kowald, S. Kopeinik, P. Seitinger, T. Ley, D. Albert, and C. Trattner: [http://www.christophtrattner.info/pubs/msm7_kowald.pdf Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context]. In Mining, Modeling, and Recommending 'Things' in Social Media, Lecture Notes in Computer Science, Vol. 8940, Springer, 2015a.
* D. Kowald, P. Seitinger, S. Kopeinik, T. Ley, and C. Trattner: [http://www.christophtrattner.info/pubs/msm8_kowald.pdf Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender]. In Mining, Modeling, and Recommending 'Things' in Social Media, Lecture Notes in Computer Science, Vol. 8940, Springer, 2015b.
* D. Kowald, P. Seitlinger, C. Trattner, and T. Ley. [http://arxiv.org/abs/1312.5111 Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency]. In Proceedings of the 23rd international conference on World Wide Web Companion, WWW '14, ACM, New York, NY, USA, 2014.
* E. Lacic, D. Kowald, P. Seitlinger, C. Trattner, and D. Parra. [http://www.christophtrattner.info/pubs/sp2014.pdf Recommending Items in Social Tagging Systems Using Tag and Time Information]. In Proceedings of the 1st Social Personalization Workshop co-located with the 25th ACM Conference on Hypertext and Social Media, HT'14, ACM, New York, NY, USA, 2014.
* P. Seitlinger, D. Kowald, C. Trattner, and T. Ley.: [http://www.christophtrattner.info/pubs/cikm2013.pdf Recommending Tags with a Model of Human Categorization]. In Proceedings of The ACM International Conference on Information and Knowledge Management (CIKM 2013), ACM, New York, NY, USA, 2013.
* A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In The semantic web: research and applications, pages 411–426. Springer, 2006.
* L. Zhang, J. Tang, and M. Zhang. Integrating temporal usage pattern into personalized tag prediction. In Web Technologies and Applications, pages 354–365. Springer, 2012.
* R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Knowledge Discovery in Databases: PKDD 2007, pages 506–514. Springer, 2007.
* R. Krestel, P. Fankhauser, and W. Nejdl. Latent dirichlet allocation for tag recommendation. In Proceedings of the third ACM conference on Recommender systems, pages 61–68. ACM, 2009.
* J. R. Anderson, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin. An integrated theory of the mind. Psychological Review, 111(4):1036–1050, 2004.
* J. K. Kruschke et al. Alcove: An exemplar-based connectionist model of category learning. Psychological review, 99(1):22–44, 1992.
* D. L Hintzman. Minerva 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers 16 (2), 96–101, 1984.
* N. Zheng and Q. Li. A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl., 2011.
* C.-L. Huang, P.-H. Yeh, C.-W. Lin, and D.-C. Wu. Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowledge-Based Systems, 2014.
* B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.

== External links ==
* https://github.com/learning-layers/TagRec

[[Category: Java]]
[[Category: Software]]

@@ Line 1: / Line 1: @@
 == Tag Recommender Benchmarking Framework ==
-''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster
+''TagRec won the best poster award @ [http://ht.acm.org/ht2014/index.php?awards.poster Hypertext 2014 (HT'14) conference ]''
 The aim of '''TagRec''' is to provide the community with a simple to use, '''generic [[tag-recommender]] framework''' written in '''Java''' to evaluate novel tag-[[recommender algorithms]] with a set of well-known std. IR metrics such as [[nDCG]], [[MAP]], [[MRR]], [[Precision]] (P@k), [[Recall]] (R@k), [[F1]]-score (F1@k), [[Diversity]] (D), [[Serendipity]] (S), [[User Coverage]] (UC) and [[folksonomy]] datasets such as BibSonomy, CiteULike, LastFM, Flickr, MovieLens or Delicious and to benchmark the developed approaches against state-of-the-art tag-recommender algorithms such as MP, MP_r, MP_u, MP_u,r, CF, APR, FR, GIRP, GIRPTM, etc.

← Older revision		Revision as of 17:02, 27 March 2015
Line 1:		Line 1:
−	== ~~Towards A Standardized~~ Tag Recommender Benchmarking Framework ==	+	== Tag Recommender Benchmarking Framework ==

	''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster		''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster

@@ Line 10: / Line 10: @@
 Based on our latest strand of research, '''TagRec also contains algorithms for the personalized recommendation of items''' in social tagging systems. In this respect TagRec includes a novel algorithm called CIRTT (Lacic et al., 2014) that integrates tag and time information using the BLL-equation coming from the ACT-R theory (Anderson et al, 2004). Furthermore, it contains another novel item-recommender called SUSTAIN+CFu (Seitlinger et al., 2015) that improves user-based CF via integrating the addentional focus of users via the SUSTAIN model (Love et al., 2004).
-RiVal specifically focuses on transparency and reproducibility.
 == Download  ==

@@ Line 3: / Line 3: @@
 ''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster
-The aim of '''TagRec''' is to provide the community with a simple to use, '''generic tag-recommender framework''' written in '''Java''' to evaluate novel tag-recommender algorithms with a set of well-known std. IR metrics such as nDCG, MAP, MRR, Precision (P@k), Recall (R@k), F1-score (F1@k), Diversity (D), Serendipity (S), User Coverage (UC) and folksonomy datasets such as BibSonomy, CiteULike, LastFM, Flickr, MovieLens or Delicious and to benchmark the developed approaches against state-of-the-art tag-recommender algorithms such as MP, MP_r, MP_u, MP_u,r, CF, APR, FR, GIRP, GIRPTM, etc.
+The aim of '''TagRec''' is to provide the community with a simple to use, '''generic [[tag-recommender]] framework''' written in '''Java''' to evaluate novel tag-[[recommender algorithms]] with a set of well-known std. IR metrics such as [[nDCG]], [[MAP]], [[MRR]], [[Precision]] (P@k), [[Recall]] (R@k), [[F1]]-score (F1@k), [[Diversity]] (D), [[Serendipity]] (S), [[User Coverage]] (UC) and [[folksonomy]] datasets such as BibSonomy, CiteULike, LastFM, Flickr, MovieLens or Delicious and to benchmark the developed approaches against state-of-the-art tag-recommender algorithms such as MP, MP_r, MP_u, MP_u,r, CF, APR, FR, GIRP, GIRPTM, etc.
 Furthermore, it contains algorithms to process datasets (e.g., p-core pruning, leave-one-out or 80/20 splitting, LDA topic creation and create input files for other recommender frameworks).

@@ Line 11: / Line 11: @@
 Based on our latest strand of research, '''TagRec also contains algorithms for the personalized recommendation of items''' in social tagging systems. In this respect TagRec includes a novel algorithm called CIRTT (Lacic et al., 2014) that integrates tag and time information using the BLL-equation coming from the ACT-R theory (Anderson et al, 2004). Furthermore, it contains another novel item-recommender called SUSTAIN+CFu (Seitlinger et al., 2015) that improves user-based CF via integrating the addentional focus of users via the SUSTAIN model (Love et al., 2004).
 RiVal specifically focuses on transparency and reproducibility.
 == Citation ==
@@ Line 38: / Line 41: @@
 * C.-L. Huang, P.-H. Yeh, C.-W. Lin, and D.-C. Wu. Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowledge-Based Systems, 2014.
 * B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.
 [[Category: Java]]
 [[Category: Software]]

@@ Line 39: / Line 39: @@
 * B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.
-== External links ==
+== Download  ==
 * https://github.com/learning-layers/TagRec