推荐引擎有哪些方法可以处理一次性、新颖且可能重要的内容？
假设您构建了一个推荐引擎，可以推荐您观看的直播电视节目。对于常规节目，您可以使用协作过滤等方法做得很好。但假设这类似于 1969 年的登月。这显然是一个重要的事件，您希望您的推荐引擎能够处理这种情况。但你也不能依赖过去的行为，因为一旦节目结束，该推荐的价值就会降至零。
在推荐空间中有哪些有效的方法来处理这个问题？
Say you built a recommendation engine that would recommend you live TV shows for you to watch. For regular shows, you could do a pretty good job using collaborative filtering and the like. But say it was something like the 1969 moon landing. It's obviously an important event, you want your recommendation engine to handle that case. But you also can't rely on past behavior since the value of that recommendation drops to zero once the show is over.
What are some effective methods to deal with this problem in the recommendation space?
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CF 中的问题通常是相反的：没有点击/评分的非常新项目却无法通过 CF 算法推荐，因此很难出现在用户面前。一件古老的、著名的物品应该很容易被推荐。
还有另一个相反的问题：一些推荐系统算法倾向于青睐每个人都知道的著名项目，而不是更长尾、不太知名的项目，这些项目在某种意义上实际上可能是更好的推荐。
听起来你觉得这个物品在某种意义上特别好。您可以通过将估计的评级值粗略地提高一定量来包含这些辅助信息。我认为有效的方法就是这样。
The problem in CF is usually the opposite: very new items with no clicks / ratings yet can't be recommended by a CF algorithm and so have trouble getting in front of users. An old, famous item ought to be easily recommendable.
There's another opposite problem: some recommender system algorithms will tend to favor the famous items that everyone knows about rather than the more long-tail, lesser-known items that may actually be better recommendations in some sense.
Sounds like you have a notion that this item is extra-good in some sense. That's side information that you could include by crudely boosting the estimated rating value by some amount. I think the effective approach is simply something like that.
你可以有一个由两部分组成的推荐引擎：
并将它们放在一起。
You could have a two-part recommendation engine:
and put them together.