We consider a generic scenario of content browsing where a client is presented with a catalogue of items of interest. Upon the selection of an item from a page of the catalogue, the client can choose the next item to browse from a list of related items presented on the same page. The system has limited resources to have all items available for immediate access by the browsing client. Therefore, it pays a penalty when the client selects an unavailable item. Conversely, there is a reward that the system gains when the client selects an immediately available item. We formulate the optimization problem of selecting the subset of items that the system should have for immediate access such that its profit is maximized, for the given system resources. We design two techniques for solving the optimization problem in linear time, as a function of the catalogue size. We examine their performance via numerical simulations that reveal their core properties. We also study their operation on actual YouTube data and compare their efficiency relative to conventional solutions. Substantial performance gains are demonstrated, due to accounting for the content graph imposed by the catalogue of items.

Figure 1. Bottom: Customer browsing a catalogue of items. Items c_i, c_j, c_k, c_m,... are selected. The provider needs to access item c_k remotely, at cost P_k, before delivering it to the customer. The provider earns profit A_i + A_j + P_k + A_m + ... Top: Graph representation of the browsing process. Filled circles represent items that the customer can access directly. The dashed arcs represent the customer's selected itinerary over the catalogue items.