## 1 Introduction  
In recent years, the rapid development of the Mobile Internet [1] and Web 2.0 [2; 3] has not only transformed people's way of life but also created new business models and economic behaviors, to enable a culture of sharing-economy. In particular, the last two decades have witnessed an explosive growth of e-commerce applications [4; 5], and a great number of users are participating in a lifestyle of shopping online and sharing data. According to the data analytics firm ComScore ([[http://www.comscore.com/](http://www.comscore.com/)]([http://www.comscore.com/](http://www.comscore.com/))), the sales on Cyber Monday, i.e., the biggest online shopping day in the US, reached $1.35 bn. In China, Alibaba has broken its own record for sales on China's Single Day in 2017, with 168.2 bn yuan (about $26.4 bn). An enormous amount of data has been produced and has grown exponentially in e-commerce applications. The large-scale data yield the Big Data analytics problem for relevant practitioners [6]. Moreover, the data of group-buying websites, a popular transaction model for online shopping [6; 7], are even more complex and dynamic due to the uncertainty, diversity and randomness of user behaviors. Group-buying, also known as collective buying, offers products and services at significantly reduced prices on the condition that a minimum number of buyers would make the purchase. Group-buying can be produced anytime and anywhere, through which discount prices are obtained from retailers when a certain group of people were willing to buy the same item. Therefore, the data of group-buying are sparser compared to that of other e-commerce models. Efficiently and effectively recommending services/items to users in such an application scenario have become a significant challenge [7]. In typical online recommendation systems, results of non-personalized recommendation lack of interpretation and are not acceptable to users because the users' personalized requirements are not considered [8]. As a result, developing effective personalized recommendation approaches is important for high-quality service recommendation. Neighborhood-based collaborative filtering (CF) approaches [9; 10; 11; 12; 13], which include user-based and item-based, have become the most popular technique for a personalized recommendation. The user-based CF approach is to find out a set of users who have similar favor patterns to a given user (i.e., \"neighbors\" of the user) and recommend to the user those items that other users in the same set like, the item-based CF approach aims to provide a user with the recommendation on an item based on the other items with high correlations (i.e., \"neighbors\" of the item). In all collaborative filtering methods, it is a significant step to find users' (or items') neighbors, that is, a set of similar users (or items). However, current CF methods suffer from such problems as data sparsity and recommendation inaccuracy. With the rapid development of Big Data techniques and platforms, effective implementation of recommendation approaches in distributed computing platforms such as MapReduce [7] has been explored, which gains good scalability and efficiency in Big Data environments. On the other hand, many studies have proven that online recommendation systems using hybrid recommendation approaches can achieve enhanced effects. Hybrid approaches mainly include collaborative filtering, demographic filtering [14], location information [15; 16], clustering [17; 18], content filtering [19] and Bayesian networks [20]. In this paper, a hybrid two-phase recommendation (TPR) method is proposed, combining collaborative filtering with clustering techniques. The main contributions of this work are summarized as follows:  
* To alleviate the data sparsity problem, item features and user behaviors were fully investigated; feature description approaches were designed to construct the feature matrix.