Name
Abstract | ||
|---|---|---|
Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1718487.1718498 | WSDM |
Keywords | Field | DocType |
personalized tag recommendation,cubic core tensor,high quality tag recommendation,cubic runtime,factorization model,graph-based tag recommendation,linear runtime,td model,better prediction quality,pairwise interaction tensor factorization,factorization dimension,factor model,collaborative filtering,recommender systems,measurement,recommender system,personalization | Data mining,Computer science,Tucker decomposition,Artificial intelligence,Recommender system,Pairwise comparison,PageRank,Collaborative filtering,Information retrieval,Ranking,Factorization,Machine learning,Bayesian probability | Conference |
Citations | PageRank | References |
310 | 9.45 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steffen Rendle | 1 | 1963 | 70.68 |
| Lars Schmidt-Thieme | 2 | 3802 | 216.58 |