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This paper calls out several research challenges in the art of recommendation technology as applied in Web media sites. One particular characteristic of such recommendation settings is the relative low cost of falsely recommending an irrelevant item, which means that recommendation schemes can be less conservative and more exploratory. This also creates opportunities for better item cold-start handling. Other technical difficulties include analyzing offline data that is heavily biased by the site's appearance, and in a related vein -- once a recommendation module's appearance has been designed -- defining the correct metrics by which to measure it. Also called out are tradeoffs between personalization and contextualization, as are novel schemes that aim at recommending sets and sequences of items.