如何使我的推荐引擎适应冷启动? [英] How do I adapt my recommendation engine to cold starts?

问题描述

我很好奇克服冷启动"问题的方法/途径是什么，当新用户或项目进入系统时，由于缺乏关于这个新实体的信息，推荐是一个问题.

I am curious what are the methods / approaches to overcome the "cold start" problem where when a new user or an item enters the system, due to lack of info about this new entity, making recommendation is a problem.

我可以考虑做一些基于预测的推荐(例如性别、国籍等).

I can think of doing some prediction based recommendation (like gender, nationality and so on).

推荐答案

也许有些时候您不应该提出建议?数据不足"应该是其中之一.

Maybe there are times you just shouldn't make a recommendation? "Insufficient data" should qualify as one of those times.

我只是不明白基于性别、国籍等"的预测建议如何不仅仅是刻板印象.

I just don't see how prediction recommendations based on "gender, nationality and so on" will amount to more than stereotyping.

IIRC，亚马逊等地方在推出建议之前建立了一段时间的数据库.这不是你想要出错的事情；有很多关于基于数据不足的不当推荐的故事.

IIRC, places such as Amazon built up their databases for a while before rolling out recommendations. It's not the kind of thing you want to get wrong; there are lots of stories out there about inappropriate recommendations based on insufficient data.

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