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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.


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}, }

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}, }


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  • D. Kowald, S. Kopeinik, P. Seitinger, T. Ley, D. Albert, and C. Trattner: 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: 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.
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