Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low-or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low-and highorder feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

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User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the sparsity problems in representation and optimization, most research focuses on feature engineering and shallow modeling. Recently, deep neural networks have attracted research attention on such a problem for their high capacity and end-to-end training scheme. In this paper, we study user response prediction in the scenario of click prediction. We first analyze a coupled gradient issue in latent vector-based models and propose kernel product to learn field-aware feature interactions. Then we discuss an insensitive gradient issue in DNN-based models and propose Product-based Neural Network (PNN) which adopts a feature extractor to explore feature interactions. Generalizing the kernel product to a net-in-net architecture, we further propose Product-network In Network (PIN) which can generalize previous models. Extensive experiments on 4 industrial datasets and 1 contest dataset demonstrate that our models consistently outperform 8 baselines on both AUC and log loss. Besides, PIN makes great CTR improvement (relatively 34.67%) in online A/B test.Many machine learning models are leveraged or proposed to work on such a problem, including linear models, latent vector-based models, tree models, and DNN-based models. Linear models, such as Logistic Regression (LR) [25] and Bayesian Probit Regression [14], are easy to implement and with high efficiency. A typical latent vector-based model is Factorization Machine (FM) [36]. FM uses weights and latent vectors to represent categories. According to their parametric representations, LR has a linear feature extractor, and FM has a bi-linear 2 feature extractor. The prediction of LR and FM are simply based on the sum over weights, thus their classifiers are linear. FM works well on sparse data, and inspires a lot of extensions, including Field-aware FM (FFM) [21]. FFM introduces field-aware latent vectors, which gain FFM higher capacity and better performance. However, FFM is restricted by space complexity. Inspired by FFM, we find a coupled gradient issue of latent vector-based models and refine feature interactions 3 as field-aware feature interactions. To solve this issue as well as saving memory, we propose kernel product methods and derive Kernel FM (KFM) and Network in FM (NIFM).Trees and DNNs are potent function approximators. Tree models, such as Gradient Boosting Decision Tree (GBDT) [6], are popular in various data science contests as well as industrial applications. GBDT explores very high order feature combinations in a non-parametric way, yet its exploration ability is restricted when feature space becomes extremely high-dimensional and sparse. DNN has also been preliminarily studied in information system literature [8,33,40,51]. In [51], FM supported Neura...

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for longrun performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RLbased methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods. Recently, reinforcement learning (RL) (Sutton and Barto 1998), which has achieved remarkable success in various

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