## 1 Introduction  
Measurements revealed that content retrieval applications (e.g., website browsing, video streaming, and file download) have contributed to the majority of traffic running on the mobile Internet. To help users obtain the most relevant and timely information, personalized content retrieval services have recently emerged as a novel type of content retrieval service. Personalized content retrieval relies on the historical user-behavior data to drive recommendation algorithms [1; 2], which can effectively process a vast content pool and select a small subset of content that would best appeal to the targeted user's interest. As an indispensable tool in the era of information explosion, personalized content retrieval technology has attracted wide research interests worldwide. The service of personalized content retrieval is provided in two consecutive phases: content recommendation phase and content delivery phase. The first phase is responsible for processing a vast amount of user-relevant contextual data [3; 4], such as historical online behavior, social relationships, and mobility patterns, to establish user profiles. The information of user profiles and content properties are then jointly processed to rate the user's potential interest on a piece of content. Contents with the highest rating can then be recommended to the user. The latter phase of content delivery is responsible for end-to-end delivery of the requested content via the communication network with certain quality-of-service (QoS) requirement. In the personalized content retrieval service, user's quality-of-experience (QoE) is related to both phases. First, at the semantic level, the recommended content should be appealing to users; second, at the perception level, the content download/viewing process should be smooth, which translates to high communications throughput and low delay. Nowadays, content recommendation and content delivery has evolved into two different industries. Players of the content recommendation industry are called over-the-top players (OTIs) [5]. These players own the (copyright of) contents and track the users' content preferences using big-data technologies. On the other hand, players of the content delivery industry are called content delivery networks (CDNs). These players build general-purpose communication infrastructures for a variety of applications (including but not limited to content delivery applications). Although such a separation is intuitive and convenient, recent studies have shown that once the tasks of content recommendation and content delivery can be jointly optimized, i.e., if OTIs and CDNs are allowed to cooperate or jointly designed, the overall performance of content retrieval can be improved significantly [6; 7]. The rationale of joint OTT and CDN design is to consider the human/user factor in the content delivery, thus is also called user-in-the-loop (UiL) content delivery. This concept has inspired a wealth of research in recent years. Most studies about UiL content delivery in the literature focused on a design paradigm called proactive caching [8; 9; 10]. Proactive caching selects a set of recommended content, pushes the content through the network when there is available capacity, and caches the contents at the user device before a user request actually happens. The proactive caching scheme has been systematically investigated in the literature, covering many issues such as multicast streaming [11; 12], energy and bandwidth efficiency [13; 14], heterogeneous overlay networks [12], dynamic traffic pricing [15; 16] and D2D communication networks [17; 18; 19]. Although the proactive caching technology does help in network load balancing and service delay reduction, it has some inherent shortcomings. First, the user devices should have a large amount of storage space, resulting in high costs and energy consumption. Second, if the pushed content is not viewed by the users eventually, the network resources used for delivering the content is wasted.