# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 6 Recommendation: Making RANs More Proactive
### After-Request Recommendation and Soft Hit  
A more adventurous attempt is the recommendation after request, also referred to as the flexible recommendation. Specifically, when a user asks for a content item that has not been cached in the memory, the RS finds some relevant content items from the local cache, if there are any, and recommend them to the user [41]. If the user accepts the recommendation, a soft hit is achieved [43]. Otherwise, on-demand transmission will be launched to satisfy the user. It is a \"win-win\" solution for both users and RANs because the user enjoys low latency, better QoS, and price reduction by reading from local cache directly, while the RAN enjoys reduced peak-rate and improved SE/EE.  
Though low latency sounds attractive, users sometimes need a stronger motivation to accept this \"win-win\" solution. A potential approach for boosting the soft hit ratio is to reduce or even waive the service fee of the recommended file. Though such a discount reduces a VNO's income from serving the recommended file, it avoids the VNO spending much more money on bidding for peak-time bandwidth. After-request recommendation brings many cross-disciplinary research opportunities. For instance, how to discover relevant content from local cache needs investigation based on NLP or other cross-domain recommendation methods. In turn, the better QoS and lower price provided by caching improve a user's willingness to accept recommendations, leading to an inherent interaction among caching, pricing, and recommendation that remains open.  
The joint design of caching, communication, and recommendation substantially broadens the concept of cross-layer design. The \"show me the cache\" policy makes a RANmuch more proactive by affecting user preferences. Instead of simply waiting for a user's command on what and when to transmit, a cache-empowered RAN tells a user what he or she might be interested in and what has been done for him/her. This paradigm-shift architecture motivates a rethinking of effective communications in Figure 6. Classic communication theory cares about whether a message is reliably transmitted, while emerging caching theory cares about whether the message is needed. Then the reliability and utility dimensions form a coordinate system, in which effective communication belongs to the first quadrant. Error detection and correction improve the reliability in the horizontal dimension. Recommendation and request prediction jointly enhance the utility of cached data in the vertical dimension.  
Cache-friendly recommendation generalizes the research topics on cross-layer designs. First, flexible recommendation might harm a user's QoE if the user is less interested in what is recommended. The Quality-of-Experience (QoE) loss due to the recommendation errors needs more experimental study. How to attain caching gains under QoE constraint emerges as a critical issue. Second, how to apply the cross-domain RS, e.g., based on NLP and social network analysis, raises many cross-disciplinary research opportunities. Third, since a user enjoys low latency and low cost by reading cached content, caching in turn improves a user's willingness to accept recommendations and promotes information propagation in social networks. The interactions between caching, recommendation, and information propagation remain open.  
Finally, the \"show me the cache\" approach allows RANs to affect users. Hence there are potential ethical problems, as users are somehow manipulated. Further, a VNO will tend to advocate common interests for popular files because they help to maximize the multicasting gain in caching. However, such techno-philosophical discussions are beyond the scope of this article.