  Caching has recently attracted a lot of attention in the wireless
communications community, as a means to cope with the increasing number of
users consuming web content from mobile devices. Caching offers an opportunity
for a win-win scenario: nearby content can improve the video streaming
experience for the user, and free up valuable network resources for the
operator. At the same time, recent works have shown that recommendations of
popular content apps are responsible for a significant percentage of users
requests. As a result, some very recent works have considered how to nudge
recommendations to facilitate the network (e.g., increase cache hit rates). In
this paper, we follow up on this line of work, and consider the problem of
designing cache friendly recommendations for long viewing sessions;
specifically, we attempt to answer two open questions in this context: (i)
given that recommendation position affects user click rates, what is the impact
on the performance of such network-friendly recommender solutions? (ii) can the
resulting optimization problems be solved efficiently, when considering both
sequences of dependent accesses (e.g., YouTube) and position preference? To
this end, we propose a stochastic model that incorporates position-aware
recommendations into a Markovian traversal model of the content catalog, and
derive the average cost of a user session using absorbing Markov chain theory.
We then formulate the optimization problem, and after a careful sequence of
equivalent transformations show that it has a linear program equivalent and
thus can be solved efficiently. Finally, we use a range of real datasets we
collected to investigate the impact of position preference in recommendations
on the proposed optimal algorithm. Our results suggest more than 30\%
improvement with respect to state-of-the-art methods.
