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## Improving Pairwise Learning for Item Recommendation from Implicit Feedback

### Citations

1495 | Empirical analysis of predictive algorithms for collaborative filtering
- BREESE, HECKERMAN, et al.
- 1998
(Show Context)
Citation Context ... the items in the test set appear. The average measure over all test context is reported where the selected measures are: average precision (MAP) with a cutoff of 1000 and the half-life utility (HLU) =-=[2]-=-. The influence of the sampled pairs is measured by the average gradient magnitude. The gradient magnitude (eq. 9) is measured for each sampled pair and the average magnitude over a training epoch is ... |

424 | Factorization meets the neighborhood: a multifaceted collaborative filtering model,”
- Koren
- 2008
(Show Context)
Citation Context ...ization models play a central role in modern recommender systems. The popular matrix factorization model (e.g. [16]), has been extended for many recommender scenarios, e.g. using implicit information =-=[10, 18]-=-, time [11], neighborhood information [10] or attributes [20]. For contextaware settings, tensor factorization approaches have been applied (e.g. [9, 13]). Whereas these works solve the rating predict... |

287 | Probabilistic matrix factorization.
- Salakhutdinov, Mnih
- 2008
(Show Context)
Citation Context ...ization models play a central role in modern recommender systems. The popular matrix factorization model (e.g. [16]), has been extended for many recommender scenarios, e.g. using implicit information =-=[10, 18]-=-, time [11], neighborhood information [10] or attributes [20]. For contextaware settings, tensor factorization approaches have been applied (e.g. [9, 13]). Whereas these works solve the rating predict... |

246 | Collaborative filtering with temporal dynamics,”
- Koren
- 2010
(Show Context)
Citation Context ...play a central role in modern recommender systems. The popular matrix factorization model (e.g. [16]), has been extended for many recommender scenarios, e.g. using implicit information [10, 18], time =-=[11]-=-, neighborhood information [10] or attributes [20]. For contextaware settings, tensor factorization approaches have been applied (e.g. [9, 13]). Whereas these works solve the rating prediction task (i... |

246 | Fast maximum margin matrix factorization for collaborative prediction
- Rennie, Srebro
- 2005
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Citation Context ...5) indicate that adaptive oversampling fulfills both requirements. 6. RELATED WORK Factorization models play a central role in modern recommender systems. The popular matrix factorization model (e.g. =-=[16]-=-), has been extended for many recommender scenarios, e.g. using implicit information [10, 18], time [11], neighborhood information [10] or attributes [20]. For contextaware settings, tensor factorizat... |

193 | Collaborative filtering for implicit feedback datasets.
- Hu, Koren, et al.
- 2008
(Show Context)
Citation Context ... work deals with item recommendation (i.e. ranking) from implicit (one-class) feedback. The item recommendation task is much harder because the optimization target is not directly observed. Hu et al. =-=[6]-=- and Pan et al. [12] investigate item recommendation from implicit feedback and propose to cast the oneclass problem into a two class problem by imputing all nonobserved values with 0 and to apply reg... |

153 | BPR: Bayesian personalized ranking from implicit feedback.
- Rendle, Freudenthaler, et al.
- 2009
(Show Context)
Citation Context ...s, learning algorithms are usually based on sampling pairs (uniformly) and applying stochastic gradient descent (SGD). This optimization framework is also known as Bayesian Personalized Ranking (BPR) =-=[14]-=-. Many recent published recommender systems use BPR for learning, including tensor factorization models for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxo... |

84 | Wsabie: Scaling up to large vocabulary image annotation.
- Weston, Bengio, et al.
- 2011
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Citation Context ...egative items are a priori given weights resulting from the problem description. In our work, the sampling probabilities are not fixed, but are generated from the current model. In the WARP algorithm =-=[22]-=-, negative items (=‘annotations’) are drawn repeatedly until the score of a drawn item is large enough. This algorithm increases the runtime because up to N ≤ |I | samples are drawn and their score is... |

77 | Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering.
- Karatzoglou, Amatriain, et al.
- 2010
(Show Context)
Citation Context ...er scenarios, e.g. using implicit information [10, 18], time [11], neighborhood information [10] or attributes [20]. For contextaware settings, tensor factorization approaches have been applied (e.g. =-=[9, 13]-=-). Whereas these works solve the rating prediction task (i.e. regression), our work deals with item recommendation (i.e. ranking) from implicit (one-class) feedback. The item recommendation task is mu... |

77 | Relation extraction with matrix factorization and universal schemas.
- Riedel, Yao, et al.
- 2013
(Show Context)
Citation Context ...own as Bayesian Personalized Ranking (BPR) [14]. Many recent published recommender systems use BPR for learning, including tensor factorization models for tag recommendation [15], relation extraction =-=[17]-=-, sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement [7], hierarchical latent factor models [1] or co-factorization machines [5]. In this paper, it is ... |

72 | Pairwise interaction tensor factorization for personalized tag recommendation. - Rendle, Schmidt-Thieme - 2010 |

68 |
Cofi rank - maximum margin matrix factorization for collaborative ranking.
- Weimer, Karatzoglou, et al.
- 2008
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Citation Context ...ll nonobserved values with 0 and to apply regression. This is similar to standard (algebraical) singular value decomposition (SVD) but includes confidence weights and L2 regularization. Weimer et al. =-=[21]-=- optimize a matrix factorization model for the ranking measure NDCG. This approach is mainly designed for data where ratings (or other ordering information on user feedback) is present. Recently, CLiM... |

66 | One-class collaborative filtering.
- Pan, Zhou, et al.
- 2008
(Show Context)
Citation Context ...em recommendation (i.e. ranking) from implicit (one-class) feedback. The item recommendation task is much harder because the optimization target is not directly observed. Hu et al. [6] and Pan et al. =-=[12]-=- investigate item recommendation from implicit feedback and propose to cast the oneclass problem into a two class problem by imputing all nonobserved values with 0 and to apply regression. This is sim... |

56 | Matchbox: Large scale online Bayesian recommendations.
- Stern, Herbrich, et al.
- 2009
(Show Context)
Citation Context ... The popular matrix factorization model (e.g. [16]), has been extended for many recommender scenarios, e.g. using implicit information [10, 18], time [11], neighborhood information [10] or attributes =-=[20]-=-. For contextaware settings, tensor factorization approaches have been applied (e.g. [9, 13]). Whereas these works solve the rating prediction task (i.e. regression), our work deals with item recommen... |

47 |
Factorization machines with libfm.
- Rendle
- 2012
(Show Context)
Citation Context ...mplemented efficiently in amortized constant time for a broad class of factorization models. First the basic idea is shown for matrix factorization and then a generalization to factorization machines =-=[13]-=- is shown. 4.1 Matrix Factorization (MF) Assume that the context C and items I are represented by categorical variables, i.e. C = {c1, c2, . . .} and I = {i1, i2, . . .} respectively. For example, eac... |

22 |
Climf: Learning to maximize reciprocal rank with collaborative less-is-more filtering.
- Shi, Karatzoglou, et al.
- 2012
(Show Context)
Citation Context ...ptimize a matrix factorization model for the ranking measure NDCG. This approach is mainly designed for data where ratings (or other ordering information on user feedback) is present. Recently, CLiMF =-=[19]-=- has been proposed for optimizing a matrix factorization model using implicit feedback datasets for the reciprocal rank measure. Bayesian personalized ranking (BPR) [14] is a generic optimization fram... |

11 | Learning to rank social update streams.
- Hong, Bekkerman, et al.
- 2012
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Citation Context ...orization and nearest-neighbor [14], tensor factorization for tag recommendation [15], factorized markov chains for sequential basket recommendation with taxonomy-awareness [8], social update streams =-=[4]-=- and relation extraction [17]. All these works on BPR use the uniform sampling assumption and thus are supposed to suffer from slow convergence. Our proposed adaptive sampler has the potential to impr... |

10 |
Supercharging recommender systems using taxonomies for learning user purchase behavior
- Kanagal, Ahmed, et al.
- 2012
(Show Context)
Citation Context ...recent published recommender systems use BPR for learning, including tensor factorization models for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxonomies =-=[8]-=-, focused matrix factorization for advertisement [7], hierarchical latent factor models [1] or co-factorization machines [5]. In this paper, it is shown that uniform sampling pairs results in slow con... |

9 | Co-factorization machines: modeling user interests and predicting individual decisions in twitter.
- Hong, Doumith, et al.
- 2013
(Show Context)
Citation Context ..., relation extraction [17], sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement [7], hierarchical latent factor models [1] or co-factorization machines =-=[5]-=-. In this paper, it is shown that uniform sampling pairs results in slow convergence, especially if the pool of items is large and the overall item-popularity is tailed. Both properties are common to ... |

7 | Personalized ranking for non-uniformly sampled items
- Gantner, Drumond, et al.
- 2012
(Show Context)
Citation Context ...ption and thus are supposed to suffer from slow convergence. Our proposed adaptive sampler has the potential to improve all existing recommender systems that are based on BPR learning. Gantner et al. =-=[3]-=- extend BPR with non-uniform sampling where the sampling probabilities for negative items are a priori given weights resulting from the problem description. In our work, the sampling probabilities are... |

3 | Latent factor models with additive and hierarchically-smoothed user preferences
- Ahmed, Kanagal, et al.
- 2013
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Citation Context ...odels for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement [7], hierarchical latent factor models =-=[1]-=- or co-factorization machines [5]. In this paper, it is shown that uniform sampling pairs results in slow convergence, especially if the pool of items is large and the overall item-popularity is taile... |

2 |
Focused matrix factorization for audience selection in display advertising
- Kanagal, Ahmed, et al.
- 2013
(Show Context)
Citation Context ...rning, including tensor factorization models for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement =-=[7]-=-, hierarchical latent factor models [1] or co-factorization machines [5]. In this paper, it is shown that uniform sampling pairs results in slow convergence, especially if the pool of items is large a... |