1. A computer-implemented method of selecting items to recommend to a user, the method comprising: identifying a plurality of items that, based on activity of the user, are deemed to be of interest to the user; for each of said items of interest, retrieving, from a pre-generated data structure that maps items to related items, a respective related items list; weighting the related items lists based on information reflective of the user's affinity for the corresponding items of interest, such that at least some of the related items lists are weighted differently than others, wherein weighting a related items list comprises weighting individual items on the related items lists, and wherein the amounts by which the items are weighted influences a likelihood that such items will be selected to recommend to the user, wherein weighting the related items lists comprises at least one of the following: (1) weighting a related items list by an amount that depends upon an amount of time that has transpired since the user performed an action that evidences the user's affinity for the corresponding item of interest, and (2) weighting a related items list by an amount that depends upon an explicit rating by the user of the corresponding item of interest; generating a recommendations list at least partly by combining the weighted related items lists, said recommendations lists including data values for respective items thereon, each data value being dependent upon (1) whether the respective item is on more than one of the related items lists, and (2) the weighting applied to the related items list or lists on which the respective item appears; and selecting, based at least partly on said data values, a subset of the items on the recommendations list to recommend to the user; said method performed by a computing system that comprises one or more physical servers.