PRODUCT RECOMMENDATION TO PROMOTE ASSET RECYCLING
In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset. The method also includes generating a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset, and training a recovery value prediction module using the plurality of training samples. Once trained, the recovery value prediction module can predict a recovery value of a provided asset.
1 . A computer implemented method to predict a recovery value of an asset, the method comprising:
receiving a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset;
generating a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset; and
training a recovery value prediction module using the plurality of training samples to predict a recovery value of a provided asset.
2 . The method of claim 1 , wherein a training sample corresponding to a recycled asset includes one or more features correlated with the recovery value of the recycled asset.
3 . The method of claim 1 , wherein the recovery value prediction module includes a regression-based model.
4 . The method of claim 3 , wherein the regression-based model includes a gradient boosting regression model.
5 . The method of claim 3 , wherein the regression-based model includes a dense neural network (DNN).
6 . The method of claim 1 , further comprising:
predicting, using a trained recovery value prediction module, a recovery value of an old asset;
identifying, using a machine learning (ML) model, one or more new products that most closely match the old asset; and
recommending the one or more new products with an offer to recycle the old asset for the predicted recovery value.
7 . The method of claim 6 , wherein the trained recovery value prediction module includes a k-nearest neighbor (k-NN) model.
8 . The method of claim 7 , wherein the one or more new products are identified using one of Euclidean distance or cosine similarity.
9 . A system comprising:
one or more non-transitory machine-readable mediums configured to store instructions; and
one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to:
receive a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset;
generate a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset; and
train a recovery value prediction module using the plurality of training samples to predict a recovery value of a provided asset.
10 . The system of claim 9 , wherein a training sample corresponding to a recycled asset includes one or more features correlated with the recovery value of the recycled asset.
11 . The system of claim 9 , wherein the recovery value prediction module includes a regression-based model.
12 . The system of claim 11 , wherein the regression-based model includes a gradient boosting regression model.
13 . The system of claim 11 , wherein the regression-based model includes a dense neural network (DNN).
14 . The system of claim 11 , wherein execution of the instructions further causes the one or more processors to:
predict, using a trained recovery value prediction module, a recovery value of an old asset;
identify, using a machine learning (ML) model, one or more new products that most closely match the old asset; and
recommend the one or more new products with an offer to recycle the old asset for the predicted recovery value.
15 . The system of claim 14 , wherein the trained recovery value prediction module includes a k-nearest neighbor (k-NN) model.
16 . The system of claim 15 , wherein the one or more new products are identified using one of Euclidean distance or cosine similarity.
17 . A computer implemented method to offer recovery of an old asset, the method comprising:
determining, using a first machine learning (ML) model, a predicted recovery value for an old asset;
identifying, using a second ML model, one or more new products that most closely match the old asset; and
recommending the one or more new products with an offer to recycle the old asset for the predicted recovery value.
18 . The method of claim 17 , wherein the first ML model is trained using a training dataset generated from historical recycling settlement data regarding a plurality of recycled assets.
19 . The method of claim 17 , wherein the first ML model includes a regression-based model.
20 . The method of claim 17 , wherein the second ML model is trained using a training dataset generated from information regarding configuration and pricing of a plurality of new products.