dc.contributor.advisor |
Ahangama S |
|
dc.contributor.author |
Widanagamage D |
|
dc.date.accessioned |
2022 |
|
dc.date.available |
2022 |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Widanagamage, D, (2022). Neural collaborative filtering based recommendation system for purchased product recommendation [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21643 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/21643 |
|
dc.description.abstract |
In order to validate that the problem exists, I followed the procedure as explained below.
First I grouped the data set with user id and the product. Then, for each user and item, I
have derived the number of views, transactions and add to cart events. Then, I have created
10 new data sets. For the first five data sets, I have assigned different weights based on
the event type (i.e. view, purchase or transaction). As for the second five data sets, they
were created with different volumes of view, transaction and purchased events. Then I
have verified that, with the presence of outliers (view events), the purchased products are
not recommended to the user. To verify this behaviour I have used Bayesian Personalized
Ranking, Neural Collaborative Filtering, Generalized Matrix Factorization, Most Pop,
Item KNN adjusted and Multi-Layer Perceptron models.
Thereafter, I have removed view data from the data set and grouped data records based on
the product and user. Next I have used a weighting scheme combined with binning to
derive a rating score.
Next, I have used four models to verify my solution. These includes, Bayesian
Personalized Ranking, Neural Collaborative Filtering, Item KNN adjusted, Generalized
Matrix Factorization and Multi-Layer Perceptron. I have used fivefold cross validation to
train the models and used a separate data set for validation. The results were promising. I
received a Hit ratio 0.275 for HR@10. This was a major improvement as, before this the
Hit ratio was near to 0. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
NEURAL COLLABORATIVE FILTERING |
en_US |
dc.subject |
PRODUCT RECOMMENDATION |
en_US |
dc.subject |
FEATURE ENGINEERING |
en_US |
dc.subject |
RECOMMENDATION SYSTEM |
en_US |
dc.subject |
INFORMATION TECHNOLOGY -Dissertation |
en_US |
dc.subject |
COMPUTER SCIENCE -Dissertation |
en_US |
dc.subject |
COMPUTER SCIENCE & ENGINEERING -Dissertation |
en_US |
dc.title |
Neural collaborative filtering based recommendation system for purchased product recommendation |
en_US |
dc.type |
Thesis-Abstract |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.degree |
MSc In Computer Science and Engineering |
en_US |
dc.identifier.department |
Department of Computer Science and Engineering |
en_US |
dc.date.accept |
2022 |
|
dc.identifier.accno |
TH5001 |
en_US |