Content Search through Comparisons

We study the problem of navigating through a database of similar objects using comparisons. This problem is known to be strongly related to the small-world network design problem. However, contrary to prior work, which focuses on cases where objects in the database are equally popular, we consider here the case where the demand for objects may be heterogeneous. We show that, under heterogeneous demand, the small-world network design problem is NP-hard. Given the above negative result, we propose a novel mechanism for smallworld design and provide an upper bound on its performance under heterogeneous demand. The above mechanism has a natural equivalent in the context of content search through comparisons, and we establish both an upper bound and a lower bound for the performance of this mechanism. These bounds are intuitively appealing, as they depend on the entropy of the demand as well as its doubling constant, a quantity capturing the topology of the set of target objects. Finally, ...

• Type: Journal
• Year: 2011
• Where: ICALP
• Authors: Amin Karbasi, Stratis Ioannidis, Laurent Massoulié
• Keywords: Adaptive Learning, ICALP 2011, Lower Bound, Programming Languages, Target Objects