Recommending Items in Social Tagging Systems Using Tag and Time Information

摘要: 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 two-step approach, where rst step potentially interesting candidate item-set is found user-based CF second ranked item-based CF. Within ranking integrate of tag usage time Base-Level Learning (BLL) equation coming from human memory theory used to determine reuse-probability words power-law forgetting function. As results our extensive evaluation conducted on datasets gathered three (BibSonomy, CiteULike MovieLens) show, tag-based via BLL also helps improve process items thus, can be realize an eective recommender outperforms two alternative algorithms which exploit information.