If you liked Herlocker et al.'s explanations paper, then you might like this paper too

We present explanation rules, which provide explanations of user-based collaborative recommendations but in a form that is familiar from item-based collaborative recommenda- tions; for example, \People who liked Toy Story also like Finding Nemo" We present an algorithm for computing ex- planation rules. We report the results of a web-based user trial that gives a preliminary evaluation of the perceived ef- fectiveness of explanation rules. In particular, we find that nearly 50% of participants found this style of explanation to be helpful, and nearly 80% of participants who expressed a preference found explanation rules to be more helpful than similar rules that were closely-related but partly-random.