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This work falls under the broad setting of coded caching with user-dependent file popularity and average-rate capacity analysis. In general, the exact capacity characterization with user-dependent file popularity remains an open problem. For example, user 1 may be interested in files 1 and 2 with probabilities 0.6 and 0.4, respectively, while user 2 may be interested in only files 2, and 3 with probabilities 1/3 and 2/3, respectively, but not interested in file 1 at all. An optimal scheme needs to carefully balance the conflicting interests under the given probabilistic weights. Motivated by this fundamental but intrinsically difficult problem, this work studies the following simplified setting: Each user k is associated with a file demand set (FDS) Θ _k ; each file in Θ _k is equally desired by user k with probability 1/|Θ _k | and files outside Θ _k is not desired at all. Different users may have different Θ _k1 ≠ Θ _k2 , which reflects the user …