## 1 Introduction  
In recent years, recommender systems have improved substantially and are now widely adopted by many online services in domains such as news, e-commerce and social media, among many others. The key to a personalized recommendation for a target user is in modeling similar users' preferences for items in a domain based on those similar users' past interactions and the similarity of their patterns to those of the target user (e.g., in ratings and clicks). In a broader sense, any use of such user similarity (sometimes called \"nearest neighbors\") is an umbrella concept known as collaborative filtering [1; 2]. With the inclusion of time-stamp and sequence, collaborative filtering is being merged with history sequences to play a vital role in many industrial recommender systems. The most well-known collaborative filtering technique, matrix factorization [3; 4; 5], projects users and items into a shared latent space and utilizes a vector of latent dimensions to represent a user or an item. Thereafter a user's interaction with an item is modeled as the inner product of their latent vectors. Recently, researchers have been embracing deep-learning neural architectures that can learn very complicated functions from data, to replace the inner product applied in matrix factorization [6; 7] and also include information from history sequences [8; 9]. However, even with (a) user-user similarity (based on, e.g., clicks), and (b) history sequences, there is still another source of information which emerges from (c) the \"attributes\" (sometime called \"features\") of users and items, and the interactions of these attributes, originally at the main effect level, but that is now moving to the second, third and even higher-order interactions. This is the landscape on which our model operates (attention-based latent information extraction network (ALIEN) for high-order feature interactions). The user's attributes include demographic factors, e.g., age, gender, occupation and educational background [7; 10; 11; 12; 13; 14]. In addition, item attributes such as the category of a product, the genre of a movie and the release date of an album, not only render the basic information about the item, but also provide clues as to why the user is interested in it [10; 15]. For example, it is reasonable to recommend _Toy Story_, a famous cartoon movie, to an eight-year-old boy [PERSON] when he enters a video streaming website. Therefore, a third-order feature interaction, <gender = male, age = 8, movie's genre = (animation, children's, comedy)>, can be an informative description of this scenario for prediction. Existing works which focused on modeling low-order feature interactions from user and item attributes have been proposed for click-through rate (CTR) prediction [16; 17; 18; 19], whose primary task is to predict the probabilities of users clicking items, e.g., advertisements, music and products. However, although the problem of feature engineering has been automated beyond manual feature selection, these models still lack the capability of extracting latent information from high-order feature interactions, which usually increases the dimensions and sparsity of the input features exponentially, leading to a more serious problem of model overfitting [20]. In addition, [PERSON] may be curious about the reason why _Toy Story_ was recommended to him. A possible assumption is, on the one hand, that it was recommended because he watched _Lion King_ last week, whose genres are (animation, children's, musical), similar to _Toy Story_'s, and welcomed by children as well. On the other hand, the third-order feature interaction <gender = male, age = 8, movie's genre = (animation, children's, musical)> has a greater impact on the recommendation of _Toy Story_ than any other feature interactions, e.g., <zipcode = 48067 and movie's year of release = 1994>.