Field-aware Factorization Machines for CTR Prediction

Juan, Yu-Chin; Zhuang, Yun; Chin, Wei-Sheng; Lin, Chih-Jen

doi:10.1145/2959100.2959134

Cited by 657 publications

(449 citation statements)

References 14 publications

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“…Using the public data, we conducted the first experiment to demonstrate the effectiveness of our proposed method. In the second experiment, we used the offline in-house data, and incorporated the IW approach into the field-aware factorization machines (FFM) [10]; subsequently we evaluated the derived method, hereafter referred to as FFMIW, on the offline in-house data, and demonstrated its superiority to the FFM under a specific circumstance. Finally, based on the offline result, we decided to conduct an online A/B test to confirm the effectiveness of our proposed method in the production system.…”

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confidence: 99%

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback

Yasui

Morishita

Komei

et al. 2020

Proceedings of the Web Conference 2020

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In display advertising, predicting the conversion rate, that is, the probability that a user takes a predefined action on an advertiser's website, such as purchasing goods is fundamental in estimating the value of displaying the advertisement. However, there is a relatively long time delay between a click and its resultant conversion. Because of the delayed feedback, some positive instances at the training period are labeled as negative because some conversions have not yet occurred when training data are gathered. As a result, the conditional label distributions differ between the training data and the production environment. This situation is referred to as a feedback shift. We address this problem by using an importance weight approach typically used for covariate shift correction. We prove its consistency for the feedback shift. Results in both offline and online experiments show that our proposed method outperforms the existing method.

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confidence: 99%

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback

Yasui

Morishita

Komei

et al. 2020

Proceedings of the Web Conference 2020

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“…items she has previously interacted with, and matrix factorization for prediction. Factorization machines [2], [4] provide a mechanism to incorporate side information such as user demographics and item attributes.…”

Section: A Model-based Collaborative Filtering Methodsmentioning

confidence: 99%

Multi-graph Convolution Collaborative Filtering

Sun

Zhang

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

119

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Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a user's preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from user-item interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process. Multi-GCCF not only expressively models the high-order information via a bipartite user-item interaction graph, but also integrates the proximal information by building and processing user-user and item-item graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several state-ofthe-art collaborative filtering and graph neural network-based recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.

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“…There has been lots of work in the literature on click and conversion prediction in online advertising. Research on click prediction focus on developing various models, including Logistic Regression (LR) [8,23,27], Polynomial-2 (Poly2) [6], tree-based models [16], tensor-based models [26], Bayesian models [13], Field-aware Factorization Machines (FFM) [18,19], and Field-weighted Factorization Machines (FwFM) [24]. Recently, deep learning for CTR prediction also attracted a lot of research attention [9,14,15,25,29,30,32].…”

Section: Related Workmentioning

confidence: 99%

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Pan

Mao

Ruiz

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.

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Field-aware Factorization Machines for CTR Prediction

Cited by 657 publications

References 14 publications

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback

Multi-graph Convolution Collaborative Filtering

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

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