Exponential time smoothing

[Darel13712]

We can add some time-awareness to models by applying time-dependent weights on relevance values. This weightening can happen at three places in the recommendation process:

1. Before model training
2. At prediction time before get_top_k method
3. After prediction, as a way to rerank final recommendations

Regardless of the option we choose (or support all of them), we should have functions that calculate these weights.

Arguments should include

• decay — the "half-life" of a weight, the number of days the weight is reduced by 50%. Probably float.
• limit — the minimal value the weight can reach, to avoid zeroing very old interactions.

There are two options to calculate weights: for each interaction and for each item.

Both take log with timestamp values as an input, but return values are different.

Item-weights return a new DataFrame mapping item_id to weight.
Interaction-weights modify log relevance values in place.