The project is Neural Collaborative filtering on movie lens 100k dataset.
The training notebook can be found under notebooks. The model is trained on 100k samples of movie lens dataset
Hit Rate (HR) : Hit Rate is a binary metric that measures the proportion of correct recommendations in a given recommendation list. It answers the question: "Did the user find at least one relevant item in the top-k recommendations?"
Normalized Discounted Cumulative Gain (nDCG): NDCG is a ranking metric that evaluates the quality of the entire ranked list of recommendations. It considers both the relevance of the items and their positions in the list.
Formulas for the both can be found under the figures directory of reports. Following are the numbers (Mean) over test set of 610 unique users not present in the training.
| HR | nDCG | layer for MLP | Epochs |
|---|---|---|---|
| 0.784 | 0.545 | [64,32,16] | 10 |
For further details please refer to the report under the reports directory.
The project is based on: Neural Collaborative filtering
@article{DBLP:journals/corr/abs-1708-05031,
author = {Xiangnan He and
Lizi Liao and
Hanwang Zhang and
Liqiang Nie and
Xia Hu and
Tat{-}Seng Chua},
title = {Neural Collaborative Filtering},
journal = {CoRR},
volume = {abs/1708.05031},
year = {2017},
url = {http://arxiv.org/abs/1708.05031},
eprinttype = {arXiv},
eprint = {1708.05031},
timestamp = {Mon, 13 Aug 2018 16:49:05 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1708-05031.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}