Field-aware Factorization Machine Research Articles

Practical differentially private online advertising

Powered by machine learning technology, online advertising achieves accurate advertisement delivery to potential customers according to online user profiles. However, it raises serious privacy concerns since the learning process may reveal sensitive information in the profiles. It is highly desirable to provide high-quality advertisement recommendations while respecting an individual’s privacy.To address the privacy issues, we propose a practical privacy-preserving system that predicts advertisement Click Through Rate (CTR) accurately without revealing any sensitive information of individuals. Our system combines both offline and online training to achieve the goal. In the offline training phase, we develop two differentially private algorithms built upon Gradient Boosting Decision Tree (GBDT) and Field-aware Factorization Machine (FFM) algorithms, respectively, to transform features and train a privacy-preserving offline model. In the online training phase, we propose a privacy-preserving online learning algorithm to compensate for the decline of offline performance when online feature changes occur.We perform extensive experiments to evaluate the performance of our system based on three real-world datasets. The results demonstrate that our system can protect user privacy without compromising the accuracy of CTR prediction, with reasonable computation overhead.

Movie Recommendation System for Educational Purposes Based on Field-Aware Factorization Machine

With rich resources, movies have been applied as instructional media in the domain of education, such as fields of Second/Foreign Language Leaning, Communication, and Media Art. Factorization machine (FM) can effectively simulate common matrix factorization models by changing the form of real-value vector, which can be utilized in movies recommendation under the context of education. However, it is usually used to solve classification tasks. This paper applies the field-aware factorization machine (FFM) to solve movie rating prediction and help users select appropriate movies for learning purposes. In order to further enhance the availability of the model, clustering algorithm is also integrated in FFM for adding new fields. The experimental results demonstrate the effectiveness of the proposed methods in reducing the RMSE.

Attention-over-Attention Field-Aware Factorization Machine

Factorization Machine (FM) has been a popular approach in supervised predictive tasks, such as click-through rate prediction and recommender systems, due to its great performance and efficiency. Recently, several variants of FM have been proposed to improve its performance. However, most of the state-of-the-art prediction algorithms neglected the field information of features, and they also failed to discriminate the importance of feature interactions due to the problem of redundant features. In this paper, we present a novel algorithm called Attention-over-Attention Field-aware Factorization Machine (AoAFFM) for better capturing the characteristics of feature interactions. Specifically, we propose the field-aware embedding layer to exploit the field information of features, and combine it with the attention-over-attention mechanism to learn both feature-level and interaction-level attention to estimate the weight of feature interactions. Experimental results show that the proposed AoAFFM improves FM and FFM with large margin, and outperforms state-of-the-art algorithms on three public benchmark datasets.

Merging visual features and temporal dynamics in sequential recommendation

With the development of social networking and mobile computing technologies, data analysis in the fashion field has increasingly focused on visual features. The main features currently used in the recommendation methods include non-visual user attributes, item attributes, explicit ratings, and implicit feedbacks. How to understand visual features and integrate them with non-visual features becomes the key to building a good recommender system. In this paper, we consider both non-visual text data and visual image data and their time dynamics to build a large-scale recommender system. An advanced visual Bayesian personalized ranking (aVBPR) model is proposed, which integrates three models. Factorized personalized Markov chains (FPMC) model is used to simulate users’ sequence behaviors, intelligent field-aware factorization machine (iFFM) model also put forward by us is used to predict users’ preferences based on non-visual features, and visual Bayesian personalized ranking (VBPR) model is used to analyze users’ visual preferences. We design a learning algorithm based on AdaGrad method to optimize model aVBPR. The high complexity of the model does not affect the performance of the system by adopting multi-thread technology in the implementation of the learning algorithm. Experimental results of two real-world datasets Women's and Men's Clothing & Accessories from Amazon demonstrate that our model can obtain better recommendation results than the recent popular models for Amazon datasets. Although the model is complicated, multi-thread technique can be used to greatly improve the speed of the implementation.

Field-Aware Neural Factorization Machine for Click-Through Rate Prediction

Recommendation systems and computing advertisements are of great value for commercial applications. Click-through rate (CTR) prediction is a critical issue because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Feature engineering is usually used to improve the click-through rate prediction; however, it heavily relies on user experience. It is difficult to construct a feature combination that can describe the complex patterns implied in the data. This paper combines the traditional feature combination methods and the deep neural networks to automate the feature combinations to improve the accuracy of the click-through rate prediction. We propose a mechanism named Field-aware Neural Factorization Machine (FNFM). This paper can have strong second-order feature interactive learning ability, such as Field-aware Factorization Machine; on this basis, a deep neural network is used for higher order feature combination learning. This experiment shows that the model has stronger expression ability than previous deep learning feature combination models, such as the DeepFM, DCN, and NFM.