Like2Vec implementation
Source:
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
The original file "ratings.dat" and is in the following format:
UserID::MovieID::Rating::Timestamp
- UserIDs range between 1 and 6040
- MovieIDs range between 1 and 3952
- Ratings are made on a 5-star scale (whole-star ratings only)
- Timestamp is represented in seconds since the epoch as returned by time(2)
- Each user has at least 20 ratings
The Log Likelihood Ratio (LLR) was created by Ted Dunning in his paper, “Accurate Methods for the Statistics of Surprise and Coincidence". The LLR relies on calculating similarity between two users or items based on statistics that revolve around occurrences related to these users or items.
To test Log-Likelihood ratio code, "ratings.dat" file had the timestamp removed with:
cat ratings.dat | awk -F "::" '{print $1","$2","$3}' > ratings_filtered.dat
"ratings_filtered.dat" is in the following format:
UserID, MovieID, Rating
The inputs for this module follow the format:
[USER-ID, MOVIE-ID, RATING]
850384,1530,3.0
513501,1530,4.0
152583,1530,3.0
The output of the module follows the format:
((USER-ID1, USER-ID2), LOG LIKELIHOOD RATIO)
((1354067,345351),0.43)
((695858,2397231),0.78)
((2050389,654334),0.15)
DeepWalk is an unsupervised learning algorithm that learns a vector representation of each vertex in a graph. Vector representations are learned using walks (usually random walks) on the vertices in the graph.
@inproceedings{Perozzi:2014:DOL:2623330.2623732,
author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
title = {DeepWalk: Online Learning of Social Representations},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '14},
year = {2014},
isbn = {978-1-4503-2956-9},
location = {New York, New York, USA},
pages = {701--710},
numpages = {10},
url = {http://doi.acm.org/10.1145/2623330.2623732},
doi = {10.1145/2623330.2623732},
acmid = {2623732},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},
}
The current RMSE module is to be split in the following classes:
