GitHub - tedchsk/hometech: [Hackathon] Property recommendations based on user's web traffic history.

Home Hackathon 2018: Recommendation System Competition

This is my work for when I participated in Home Hackathon 2018: recommendation system competition. Given the data of user access logs from January to February 23rd, the goal is to predict the next new pages that each user will visit during the last week of February. Only pages that each user has not visited during the training period are treated as a new page. I got the "Technical Excellence" award from this hackathon.

Competition Detail (in Thai): [Link1] [Link2]

Result

Result Presentation: [Slide]

Data prepration

Data Exploration: [notebook]
Data Preparation: [notebook]
Bot data removal: [notebook]
Item2Vec Embeddings from user views: [notebook]
Doc2Vec Embeddings from descriptions: [notebook]

Prediction

Baseline (Most frequent properties): [notebook]
Matrix factorization (SVD): [notebook]
Item2Vec embeddings: [notebook]
LSTM: [notebook]
GRU: [notebook]

Name		Name	Last commit message	Last commit date
.ipynb_checkpoints		.ipynb_checkpoints
BiLSTM NER.ipynb		BiLSTM NER.ipynb
DeepLSTM.ipynb		DeepLSTM.ipynb
Ensemble.ipynb		Ensemble.ipynb
KNN .ipynb		KNN .ipynb
Matrix Factorisation collaborative filtering-Copy1.ipynb		Matrix Factorisation collaborative filtering-Copy1.ipynb
Matrix Factorisation collaborative filtering.ipynb		Matrix Factorisation collaborative filtering.ipynb
Naive most frequent.ipynb		Naive most frequent.ipynb
No bot Matrix Factorisation collaborative filtering.ipynb		No bot Matrix Factorisation collaborative filtering.ipynb
Note.ipynb		Note.ipynb
Output Analysis.ipynb		Output Analysis.ipynb
Untitled.ipynb		Untitled.ipynb
User2Vec, Project2Vec.ipynb		User2Vec, Project2Vec.ipynb
[* Train] LSTM Multiclass.ipynb		[* Train] LSTM Multiclass.ipynb
[* Train] LSTM Multilabel.ipynb		[* Train] LSTM Multilabel.ipynb
[**Train] Item2Vec.ipynb		[**Train] Item2Vec.ipynb
[**Train] Last GRU-Customloss.ipynb		[**Train] Last GRU-Customloss.ipynb
[**Train] Last GRU.ipynb		[**Train] Last GRU.ipynb
[*Evaluate] LSTM Evaluate.ipynb		[*Evaluate] LSTM Evaluate.ipynb
[Clean] 1. Prepare data.ipynb		[Clean] 1. Prepare data.ipynb
[Clean] Remove bot.ipynb		[Clean] Remove bot.ipynb
[Evaluation] Deep Evaluation.ipynb		[Evaluation] Deep Evaluation.ipynb
[Evaluation] Measure MAP.ipynb		[Evaluation] Measure MAP.ipynb
[Evaluation] Prepare Test data.ipynb		[Evaluation] Prepare Test data.ipynb
[Evaluation].ipynb		[Evaluation].ipynb
[Explore] 1. Overview.ipynb		[Explore] 1. Overview.ipynb
[Explore] 2. Surprise Lib.ipynb		[Explore] 2. Surprise Lib.ipynb
[Explore] 3. Deep features extraction.ipynb		[Explore] 3. Deep features extraction.ipynb
[Explore] Output Analysis.ipynb		[Explore] Output Analysis.ipynb
[Feature] Explore and combine.ipynb		[Feature] Explore and combine.ipynb
[Feature] Feature combination and extraction.ipynb		[Feature] Feature combination and extraction.ipynb
[Feature] Project Feature combination and extraction.ipynb		[Feature] Project Feature combination and extraction.ipynb
[Feature] User Feature extraction.ipynb		[Feature] User Feature extraction.ipynb
[Filtered] Select Feature from projects.ipynb		[Filtered] Select Feature from projects.ipynb
[Train] Deep Regression Model.ipynb		[Train] Deep Regression Model.ipynb
[Train] Deep Similarity Model.ipynb		[Train] Deep Similarity Model.ipynb
[Train] Deep Simple Model.ipynb		[Train] Deep Simple Model.ipynb
[Train] Doc2Vec.ipynb		[Train] Doc2Vec.ipynb
[Train] Similarity Model-Copy1.ipynb		[Train] Similarity Model-Copy1.ipynb
readme.md		readme.md

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Home Hackathon 2018: Recommendation System Competition

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