TY  - CONF
AU  - Doerfel, Stephan
AU  - Jäschke, Robert
A2  - 
T1  - An analysis of tag-recommender evaluation procedures
T2  - Proceedings of the 7th ACM conference on Recommender systems
PB  - ACM
C1  - New York, NY, USA
PY  - 2013/
CY  -  
VL  - 
IS  - 
SP  - 343
EP  - 346
UR  - https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf
DO  - 10.1145/2507157.2507222
KW  - 2013
KW  - bibsonomy
KW  - bookmarking
KW  - collaborative
KW  - core
KW  - evaluation
KW  - folkrank
KW  - folksonomy
KW  - graph
KW  - iteg
KW  - itegpub
KW  - l3s
KW  - recommender
KW  - social
KW  - tagging
L1  - 
SN  - 978-1-4503-2409-0
N1  - 
N1  - 
AB  - Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
ER  -

TY  - JOUR
AU  - Landia, Nikolas
AU  - Doerfel, Stephan
AU  - Jäschke, Robert
AU  - Anand, Sarabjot Singh
AU  - Hotho, Andreas
AU  - Griffiths, Nathan
T1  - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations
JO  - cs.IR
PY  - 2013/
VL  - 1310.1498
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/1310.1498
DO  - 
KW  - 2013
KW  - bookmarking
KW  - collaborative
KW  - folkrank
KW  - folksonomy
KW  - graph
KW  - iteg
KW  - itegpub
KW  - l3s
KW  - recommender
KW  - social
KW  - tagging
L1  - 
SN  - 
N1  - 
N1  - 
AB  - The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
ER  -