TESTING AND DEVELOPING E-COMMERCE RECOMMENDATION SYSTEMS USING RNN BP AND BPTT ALGORITHM BASED ON USER SEARCH AND PURCHASE ORDER
E-commerce is a digital platform that provides hundreds of millions of products to buy online. The large number of products makes it difficult for users to decide which product they want to buy. These problems can be solved using a recommendation system. The recommendation system can display rele...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/49890 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | E-commerce is a digital platform that provides hundreds of millions of products to buy online.
The large number of products makes it difficult for users to decide which product they want to
buy. These problems can be solved using a recommendation system. The recommendation
system can display relevant products to users so that it is easier for users to find and buy certain
products. The recommendation system that is commonly used today is matrix factorization
which does not pay attention to purchase order as a factor that determines recommendations.
In this final project, deep learning-based RNN-BP and RNN-BPTT algorithms have been tested
to make recommendations based on user search and purchase data sequences. The first test is
done by comparing the recall value@20 and mrr@20 generated by each algorithm. The
algorithm with the best accuracy value is optimized by changing the batch-size, drop-out,
learning-rate values. The second test is carried out to see the effect of clustering on the accuracy
value of the recommendations generated by the system.
Testing of the RNN-BP and RNN-BPTT algorithms shows that the RNN-BP algorithm
produces an accuracy value that is more optimal than the RNN-BPTT for the three datasets.
The most optimal value is generated by the Yoochoose Gmb dataset with recall@20, which is
58.27% and mrr@20, which is 23.77%. The optimal parameter value of the RNN-BP algorithm
was obtained using a mini-batch value of 100, a dropout value of 0.75, a learning rate of 0.001.
The optimal parameter value of the RNN-BPTT algorithm was obtained using a mini-batch
value of 50, a dropout value of 0.25, and a learning rate of 0.001. The clustering treatment of
the dataset shows an insignificant decrease in the accuracy value in the experiment with the
RNN-BP algorithm. However, the clustering treatment in the RNN-BPTT experiment showed
an increase in the accuracy value.
Keywords: E-Commerce, rnn, clusteri |
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