如何快速套用到我们的场景 about gorse HOT 5 OPEN

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We are a start-up company and are preparing to build a simple recommendation system using Gorse, but we encountered a problem. I noticed that the similar algorithm above uses more of the characteristics of the item itself as the label, and does not use the popularity of the item (only positive feedback, but does not pay attention to those secondary indicators that are not the final optimization goal).
For example, when we make video recommendations, we usually focus on the classification of the content itself, what topics it has, who the author is, etc. (These are relatively easy to apply as content tags). Many times we will also pay attention to the click-through rate, completion rate, per capita viewing time and other indicators of the content itself. How are these latter consumer characteristics reflected?

In addition, what if we want to do multi-objective weighting for the final optimization goal?

Thanks for the answer.

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可以自己配置正向因素的

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You can configure the positive factors yourself

from gorse.

可以自己配置正向因素的

但是不能配置正向因数的权重把，反馈也有分权重，比如可能更在意完播率

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You can configure the positive factors yourself

However, the weight of the forward factor cannot be configured. Feedback also has weights. For example, you may be more concerned about the completion rate.

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