## 1 Introduction  
In recent years, we have witnessed the rapid growth of neural networks for a variety of applications in computer vision, natural language processing, speech recognition, etc. Neural network provides four key benefits: (1) It works with unlabeled data; (2) It learns low-level features from minimally processed raw data; (3) It detects complex interactions among features; and (4) It works with large class memberships [1, 2]. Recent achievements have shown that it is also of great use to combine different neural networks into recommender systems. There are many works which employed neural networks to improve the recommendation in the domain of recommender systems [3, 4, 5, 6, 7] and in the context of web-based decisions [8, 9, 10, 11, 12]. The success of these recommendation approaches based on neural networks demonstrates its capability. Session-based recommender systems are good examples. Although not a novel research topic, session-based recommender systems are largely under-investigated [3, 4, 13]. Compared with traditional recommendation methods, a session-based recommender system is more appropriate for capturing dynamic and sequential user behavior. The Recurrent Neural Network (RNN) plays an important role in session modeling, and it seems to have promise for further improvement. An RNN, compared to many other recommendation models, takes advantage of the ordered sequence in a very natural way [3, 5, 7]. Automated and personalized recommendations of the type \"You may also be interested in\" are common on e-commerce sites nowadays, and there exists sufficient evidence that such item related recommendations can measurably impact businesses [14; 15; 16; 17; 18; 19]. Undoubtedly, one of the representative business scenarios is in e-commerce. Modern online shops are not static catalogs of items anymore but tend to fulfil the user's interests by providing personalized product recommendations. In the best case, these recommendations should match both the users' long-term preference as well as their current shopping goals. On Amazon.com, for example, each product page contains multiple personalized recommendation lists with different purposes. Though the Amazon recommender system employs collaborative filtering approach [20], it also uses solo-user recommendations as well. It provides alternatives to the product currently viewed, remind the user of items recently viewed, or render recommendations that should appeal to the general taste of the user, as shown in Figure 1.  
As a result, the content displayed on the page not only depends on the features of the currently viewed article (i.e., the product category), but can also be influenced by an integration of the user's historical shopping behavior (long-term preference) and his most recent interactions of viewing different products (short-term shopping goals). In this case, we assume that user's preference is composed of two components: (1) the long-term preference which reflects the considerably stable interests of the users based on their online activities, and (2) the short-term intent which represents the users' current interests. There are many existing techniques that investigate the combination of the user's long-term preference and the short-term intent [13; 21; 22; 23; 24; 25; 26; 27]. In addition, the user's preference for items constantly evolves over time [28], while these works implicitly assume the user preference to be stationary, which seems to be an unrealistic assumption in many scenarios, particularly in the news or shopping related scenarios [5; 6; 7; 13; 29]. Therefore, such methods perform poorly when preferences, in fact, are context-sensitive and transient. There are session-based approaches [7; 29; 30; 31; 32] that focus on the interest drifts, to solve the problem of the static user preference and intent, while these methods assume that each item in one session has the same influence, which is usually an invalid assumption, especially when considering a user's interactions with items of diverse characteristics.