Calculates trend score for products based on their position history.
Maintainer: Silvia
Host: osa_data
DB: meta_db
reads from:
shop_id and last_visit not older than 30 days)shop_id and session not older than 30 days)writes into:
picalike_id, sesssion))The product trend calculator receives commands and sends stats to the shop conveyor belt through port [tdb]. It follows the feed import in the pipeline.
The trend score is calculated for all products in meta_db_collection that have last_visit not older than 30 days. Likewise, only sessions not older than 30 days are taken into account for position history and category size.
Categories are weighted accordingly to their size in the last relevant session (i.e., the latest session in which the category appears) and the sum of the elements for all categories in their last relevant session.
category A: 10 items
category B: 5 items
category A: does not appear
category B: 10 items
category C: 20 items
category A: session 1 (10 items)
category B: session 2 (10 items)
category C: session 2 (20 items)
category A: 10/40 = 0.25
category B: 10/40 = 0.25
category c: 20/40 = 0.5
Sessions are weighted according to their distance to the current session measured in weeks as exp(-weeks) and normalized.
position: 0.9
weeks since current session: 1.5
time weight: exp(-1.5) = 0.2
result: 0.9*0.2 = 0.18
position: 0.4
weeks since current session: 0.5
time weight: exp(-0.5) = 0.6
result: 0.4*0.6 = 0.24
position: 0.6
weeks since current session: 0
time weight: exp(0) = 1
result: 0.6*1 = 0.6
(0.18 + 0.24 + 0.6)/(0.2 + 0.6 + 1) = 1.02/1.8 = 0.57
The product trend score is given by the sum of the category scores divided by the sum of the category weights for all categories in which the product appears in the relevant sessions.
For a given category, the category score is given by the category weight multiplied with the time weighted position.
category weight: 0.4
time weighted position: 0.5
category score: 0.4 * 0.5 = 0.2
category weight: 0.2
time weighted position: 0.9
category score: 0.2 * 0.9 = 0.18
sum(category scores)/sum(category weights): (0.2 + 0.18)/(0.4 + 0.2) = 0.38/0.6 = 0.63
Too big chunksize can lead to memory problems.