Name
Abstract | ||
|---|---|---|
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1557019.1557100 | KDD |
Keywords | Field | DocType |
optimal ranking,tag recommendation,actual problem,state-of-the-art tag recommendation method,method rtf,prediction runtime,prediction method,factorization model,tensor factorization,tf method,factorization dimension,ranking,missing values,factor model,gradient descent,singular value | Pairwise comparison,Data mining,PageRank,Gradient descent,Ranking,Computer science,Artificial intelligence,Factorization,Higher-order singular value decomposition,Missing data,Optimization problem,Machine learning | Conference |
Citations | PageRank | References |
192 | 6.19 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steffen Rendle | 1 | 1161 | 38.28 |
| Leandro Balby Marinho | 2 | 702 | 35.57 |
| Alexandros Nanopoulos | 3 | 1856 | 95.35 |
| Lars Schmidt-Thieme | 4 | 3802 | 216.58 |