https://recsyswiki.com/index.php?title=TagRec&feed=atom&action=history
TagRec - Revision history
2024-03-29T04:35:42Z
Revision history for this page on the wiki
MediaWiki 1.34.2
https://recsyswiki.com/index.php?title=TagRec&diff=2306&oldid=prev
Alan at 11:19, 31 July 2015
2015-07-31T11:19:38Z
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 11:19, 31 July 2015</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Tag Recommender Benchmarking Framework ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Tag Recommender Benchmarking Framework ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ <del class="diffchange diffchange-inline">Hypertext 2014 (HT'14) conference:'' </del>http://ht.acm.org/ht2014/index.php?awards.poster</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ <ins class="diffchange diffchange-inline">[</ins>http://ht.acm.org/ht2014/index.php?awards.poster <ins class="diffchange diffchange-inline">Hypertext 2014 (HT'14) conference ]''</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td></tr>
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Alan
https://recsyswiki.com/index.php?title=TagRec&diff=2305&oldid=prev
Alan at 11:18, 31 July 2015
2015-07-31T11:18:21Z
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 11:18, 31 July 2015</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">RiVal specifically focuses on transparency and reproducibility.</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Download ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Download ==</div></td></tr>
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Alan
https://recsyswiki.com/index.php?title=TagRec&diff=2304&oldid=prev
Alan at 11:17, 31 July 2015
2015-07-31T11:17:31Z
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 11:17, 31 July 2015</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>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.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The aim of '''TagRec''' is to provide the community with a simple to use, '''generic <ins class="diffchange diffchange-inline">[[</ins>tag-recommender<ins class="diffchange diffchange-inline">]] </ins>framework''' written in '''Java''' to evaluate novel tag-<ins class="diffchange diffchange-inline">[[</ins>recommender algorithms<ins class="diffchange diffchange-inline">]] </ins>with a set of well-known std. IR metrics such as <ins class="diffchange diffchange-inline">[[</ins>nDCG<ins class="diffchange diffchange-inline">]]</ins>, <ins class="diffchange diffchange-inline">[[</ins>MAP<ins class="diffchange diffchange-inline">]]</ins>, <ins class="diffchange diffchange-inline">[[</ins>MRR<ins class="diffchange diffchange-inline">]]</ins>, <ins class="diffchange diffchange-inline">[[</ins>Precision<ins class="diffchange diffchange-inline">]] </ins>(P@k), <ins class="diffchange diffchange-inline">[[</ins>Recall<ins class="diffchange diffchange-inline">]] </ins>(R@k), <ins class="diffchange diffchange-inline">[[</ins>F1<ins class="diffchange diffchange-inline">]]</ins>-score (F1@k), <ins class="diffchange diffchange-inline">[[</ins>Diversity<ins class="diffchange diffchange-inline">]] </ins>(D), <ins class="diffchange diffchange-inline">[[</ins>Serendipity<ins class="diffchange diffchange-inline">]] </ins>(S), <ins class="diffchange diffchange-inline">[[</ins>User Coverage<ins class="diffchange diffchange-inline">]] </ins>(UC) and <ins class="diffchange diffchange-inline">[[</ins>folksonomy<ins class="diffchange diffchange-inline">]] </ins>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.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td></tr>
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Alan
https://recsyswiki.com/index.php?title=TagRec&diff=2270&oldid=prev
Ctrattner: /* Towards A Standardized Tag Recommender Benchmarking Framework */
2015-03-27T17:02:19Z
<p><span dir="auto"><span class="autocomment">Towards A Standardized Tag Recommender Benchmarking Framework</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 17:02, 27 March 2015</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>== <del class="diffchange diffchange-inline">Towards A Standardized </del>Tag Recommender Benchmarking Framework ==</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>== Tag Recommender Benchmarking Framework ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster</div></td></tr>
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Ctrattner
https://recsyswiki.com/index.php?title=TagRec&diff=2266&oldid=prev
Ctrattner at 12:04, 27 March 2015
2015-03-27T12:04:13Z
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 12:04, 27 March 2015</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l11" >Line 11:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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).</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>RiVal specifically focuses on transparency and reproducibility.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>RiVal specifically focuses on transparency and reproducibility.</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Download ==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* https://github.com/learning-layers/TagRec</ins></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.</div></td></tr>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">== Download ==</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">* https://github.com/learning-layers/TagRec</del></div></td><td colspan="2"> </td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category: Java]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category: Java]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category: Software]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category: Software]]</div></td></tr>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 12:03, 27 March 2015</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
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Ctrattner
https://recsyswiki.com/index.php?title=TagRec&diff=2264&oldid=prev
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
<p>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..."</p>
<p><b>New page</b></p><div>== Towards A Standardized Tag Recommender Benchmarking Framework ==<br />
<br />
''TagRec won the best poster award @ Hypertext 2014 (HT'14) conference:'' http://ht.acm.org/ht2014/index.php?awards.poster<br />
<br />
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.<br />
<br />
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).<br />
<br />
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).<br />
<br />
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).<br />
RiVal specifically focuses on transparency and reproducibility.<br />
<br />
== Citation ==<br />
* 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. <br />
<br />
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}, }<br />
<br />
* 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.<br />
<br />
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}, }<br />
<br />
== Literature ==<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* 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.<br />
* J. K. Kruschke et al. Alcove: An exemplar-based connectionist model of category learning. Psychological review, 99(1):22–44, 1992.<br />
* D. L Hintzman. Minerva 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers 16 (2), 96–101, 1984.<br />
* N. Zheng and Q. Li. A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl., 2011.<br />
* 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.<br />
* B. C. Love, D. L. Medin, and T. M. Gureckis. Sustain: A network model of category learning. Psychological review, 111(2):309, 2004.<br />
<br />
== External links ==<br />
* https://github.com/learning-layers/TagRec<br />
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[[Category: Java]]<br />
[[Category: Software]]</div>
Ctrattner