17 research outputs found
Recommending Items in Social Tagging Systems Using Tag and Time Information
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 data-sets 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.Comment: 6 pages, 2 tables, 9 figure
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study
In this paper, we present the results of an online study with the aim to shed
light on the impact that semantic context cues have on the user acceptance of
tag recommendations. Therefore, we conducted a work-integrated social
bookmarking scenario with 17 university employees in order to compare the user
acceptance of a context-aware tag recommendation algorithm called 3Layers with
the user acceptance of a simple popularity-based baseline. In this scenario, we
validated and verified the hypothesis that semantic context cues have a higher
impact on the user acceptance of tag recommendations in a collaborative tagging
setting than in an individual tagging setting. With this paper, we contribute
to the sparse line of research presenting online recommendation studies.Comment: 2 pages, poste