Field-aware factorization machines for CTR prediction
Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, fieldaware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.
Code (3)
Tasks
Click-Through Rate PredictionPredictionRecommendation SystemsSimilar Papers 제목 키워드 기반
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one …
Click-Through Rate PredictionPredictionField-aware Factorization Machines in a Real-world Online Advertising System
Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and i…
A Dual Input-aware Factorization Machine for CTR Prediction
Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found …
Click-Through Rate Prediction$L_0$ Regularization of Field-Aware Factorization Machine through Ising Model
We examined the use of the Ising model as an $L_0$ regularization method for field-aware factorization machines (FFM). This approach improves generalization performance and has the advantage of simultaneously determining…
Field-Embedded Factorization Machines for Click-through rate prediction
Click-through rate (CTR) prediction models are common in many online applications such as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwF…
Click-Through Rate PredictionPredictionRecommendation Systems