RECOMMENDATION SYSTEM ASSOCIATED WITH AN ONLINE MARKETPLACE
A recommendation system associated with an online marketplace can generate recommendations pertaining to users and assets of the online marketplace by computing user-asset propensity scores. The recommendation system can compute a corresponding user-asset propensity score for each unique user-asset combination. Based on the scores, recommendations indicating predicted user behavior can be generated. To generate the user-asset propensity scores, the recommendation system can generate machine-learned models based on training data. The model can comprise a plurality of classifiers each being associated with user-asset attributes.
1 . A method for generating recommendations related to an online marketplace, the method being implemented by one or more processors and comprising:
generating, based on training data associated with the online marketplace, a model comprising a plurality of classifiers;
for a user of the online marketplace and an asset for sale on the online marketplace:
computing outputs for the plurality of classifiers based on user data associated with the user and asset data associated with asset; and
computing a user-asset propensity score based on the plurality of classifier outputs.
2 . The method of claim 1 , further comprising generating a recommendation regarding predicted behavior of the user based on the user-asset propensity score.
3 . The method of claim 2 , wherein generating the recommendation comprises comparing the user-asset propensity score against a metric.
4 . The method of claim 3 , wherein the metric is a second user-asset propensity score.
5 . The method of claim 1 , wherein each of the plurality of classifiers is a decision tree.
6 . The method of claim 1 , wherein each of the plurality of classifiers is associated with a random subset of user-asset attributes.
7 . The method of claim 1 , wherein the model is a Random Forest Model.
8 . The method of claim 1 , wherein computing the user-asset propensity score comprises averaging the plurality of classifier outputs.
9 . The method of claim 1 , wherein the training data indicates historical user activity.
10 . A recommendation system for an online marketplace comprising:
one or more processors; and
one or more memory resources storing instructions that, when executed by the one or more processors, cause the recommendation system to:
generate, based on training data associated with the online marketplace, a model comprising a plurality of classifiers;
for a user of the online marketplace and an asset for sale on the online marketplace:
compute outputs for the plurality of classifiers based on user data associated with the user and asset data associated with asset; and
compute a user-asset propensity score based on the plurality of classifier outputs.
11 . The method of claim 10 , wherein the executed instructions further cause the recommendation system to generate a recommendation regarding predicted behavior of the user based on the user-asset propensity score.
12 . The method of claim 11 , wherein generating the recommendation comprises comparing the user-asset propensity score against a metric.
13 . The method of claim 12 , wherein the metric is a second user-asset propensity score.
14 . The method of claim 10 , wherein each of the plurality of classifiers is a decision tree.
15 . The method of claim 10 , wherein each of the plurality of classifiers is associated with a random subset of user-asset attributes.
16 . The method of claim 10 , wherein the model is a Random Forest Model.
17 . The method of claim 10 , wherein computing the user-asset propensity score comprises averaging the plurality of classifier outputs.
18 . The method of claim 10 , wherein the training data indicates historical user activity.
19 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
generate, based on training data associated with the online marketplace, a model comprising a plurality of classifiers;
for a user of the online marketplace and an asset for sale on the online marketplace:
compute outputs for the plurality of classifiers based on user data associated with the user and asset data associated with asset; and
compute a user-asset propensity score based on the plurality of classifier outputs.