%0 Conference Paper %1 lacic2014recommending %A Lacic, Emanuel %A Kowald, Dominik %A Seitlinger, Paul %A Trattner, Christoph %A Parra, Denis %B Hypertext 2014 Extended Proceedings Late-breaking Results, Doctoral Consortium and Workshop Proceedings of the 25th ACM Hypertext and Social Media Conference (Hypertext 2014) Santiago, Chile, September 1-4, 2014. %D 2014 %E Cena, Federica %E da Silva, Altigran Soares %E Trattner, Christoph %I CEUR-WS.org %K based bll item memory publications recency recommender time %T Recommending Items in Social Tagging Systems Using Tag and Time Informations %U http://ceur-ws.org/Vol-1210/ %V 1210 %X In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on datasets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.

@inproceedings{lacic2014recommending, abstract = {In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on datasets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.}, added-at = {2015-07-16T12:13:32.000+0200}, author = {Lacic, Emanuel and Kowald, Dominik and Seitlinger, Paul and Trattner, Christoph and Parra, Denis}, biburl = {https://www.bibsonomy.org/bibtex/2d8e77f4b1dfb14451d1c9a5c1369f5f5/sdo}, booktitle = {Hypertext 2014 Extended Proceedings Late-breaking Results, Doctoral Consortium and Workshop Proceedings of the 25th ACM Hypertext and Social Media Conference (Hypertext 2014) Santiago, Chile, September 1-4, 2014.}, editor = {Cena, Federica and da Silva, Altigran Soares and Trattner, Christoph}, interhash = {37d3313fd27124a4341587b98f04373b}, intrahash = {d8e77f4b1dfb14451d1c9a5c1369f5f5}, keywords = {based bll item memory publications recency recommender time}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, timestamp = {2015-07-16T12:13:32.000+0200}, title = {Recommending Items in Social Tagging Systems Using Tag and Time Informations}, url = {http://ceur-ws.org/Vol-1210/}, volume = 1210, year = 2014 }