edihb Goto Github PK

atf-checker

collaborative-filtering

The increasing importance of the web as a medium for electronic and business transactions has served as a driving force for the development of recommender systems technology. An important catalyst in this regard is the ease with which the web enables users to provide feedback about their likes or dislikes. The basic idea of recommender systems is to utilize these user data to infer customer interests. The entity to which the recommendation is provided is referred to as the user, and the product being recommended is referred to as an item. The basic models for recommender systems works with two kinds of data: 1. User-Item interactions such as ratings 2. Attribute information about the users and items such as textual profiles or relevant keywords Models that use type 1 data are referred to as collaborative filtering methods, whereas models that use type 2 data are referred to as content based methods. In this project, we will build recommendation system using collaborative filtering methods.

deeprl-informationextraction

Code for the paper "Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning" http://arxiv.org/abs/1603.07954

edihb.github.io

Build a Jekyll blog in minutes, without touching the command line.

emoji_predictor

Contains a variety of nlp methods to predict emojis

large-scale-dm

pyoracc

Python tools for working with ORACC

stock-analysis

stock-prediction

This project is experimental effort to show predictions of various different stocks using various different prediction techniques and is written is R language.

summary

test-repo

text-summarization