# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 1 Introduction  
E-government refers to providing online applications to enhance the access to, and the delivery of, government information and service to the public for citizens by using modern technologies [1; 2]. As more and more professional services are loaded on the website, however, it is harder for ordinary citizens without expertise to quickly find the target service from hundreds of service items in a certain scenario of e-government because of information overload [3; 4]. The existing solutions for the e-government platforms are personalized recommendations that aim to find out target service items for the users based on their preferences, behaviors, and other information [5]. The collaborative filtering (CF) algorithm is one of the most widely used recommender algorithms since it collectively learns users and items latent representations from the user-item rating matrix [5; 6; 7]. In most recommender models, this matrix is binary with a value of 1 representing implicit feedback (e.g., click, browse, and collection) between a user and an item, and a value of 0 otherwise. In the e-government recommendation scenarios, the users' preferences also are implicit feedback--such as document browsing, online certificate status query, and new-born settlement. However, most existing e-government recommendation models need an explicit indication of users' preference (i.e., ratings) which most e-government platforms are unable to provide. Therefore, how to learn users' preferences from implicit feedback based on CF is key to the e-government recommender task. Advanced CF models focus on the use of embedding technology to obtain users and items latent features, such as matrix factorization (MF) [5; 6] and neural networks [8; 9; 10]. Neural collaborative filtering [6] (NCF) is a popular pointwise CF algorithm for recommendation with implicit feedback, which leverages the flexibility and non-linearity of neural networks to replace dot products of matrix factorization. In specific, this model is structured with two subnetworks including generalized matrix factorization (GMF) and multilayer perceptron (MLP) and models the interactions from two pathways instead of simple dot products. After the NCF, many algorithms have been proposed, which have mainly focused on how to enhance the expressiveness of users and items embedding features with the implicit data [11; 12; 13]. However, together with NCF, they have made a premise of user-item interactions: for a user, he disliked un-interacted items. They ignored the effect of negative items on positive items. In this paper, we argue that this premise may hinder the expression of interacted items and our understanding of user's needs, that is some unrated items should be viewed as the potential preference for the user. Consider the following example: a user queried the tourist flow on the e-government website, and unclicked other services. As shown in Figure 1: intuitively, the user will book a travel appointment in the future, which means the service \"appointment\" may be the false-negative item. When training the recommender model, such potential preferences may enhance the embedding feature of items and lead to a better result. This study aims to return a list of recommended items while relaxing the assumption that all the un-interaction items are not the users' preferences in training. In this work, we also treat the implicit feedback as a binary rating matrix. Factually, it is hard to know whether an un-interaction item is true negative or false negative. In our solution, two strategies are designed: negative items sampling and negative mixing. In the negative items sample, we try to construct a function to evaluate a user's preference for the negative items, and then select a better one from them. In negative mixing, we introduce a mixing method to enhance the embedding of positive items by injecting the information from negative items into them.