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Machine Learning, Natural Language Processing (NLP), Recommender System, Sentiment Analysis »

[28 Mar 2017 | Comments Off on Guide to Recommender System research containing Sentiment Analysis & Machine Learning]

Below is the step-by-step beginner guide to conduct experiment on any Recommender System research that contains some work on Natural Language Processing (NLP) as well. So, this can be a guide to NLP research work as well specifically for Sentiment Analysis. Recommender System research can include users’ reviews text and process them using Sentiment Analysis & Machine Learning techniques.
Before starting the research on Recommender Systems or NLP experiment with data, we should be very clear about the things highlighted below. If these things are understood properly then it will be …

Python, Recommender System »

[2 May 2016 | One Comment]

python-recsys is a Python Library for implementing a Recommender System.
Currently, python-recsys supports two Recommender Algorithms: Singular Value Decomposition (SVD) and Neighborhood SVD.

JAVA, Recommender System »

[25 Apr 2016 | 2 Comments]

LibRec is a promising JAVA library for Recommender Systems. It implements a lot of Recommender Algorithms. It consists of three major components: Generic Interfaces, Data Structures and Recommendation Algorithms.

JAVA, Recommender System »

[14 Mar 2016 | Comments Off on Recommender System using JAVA & Apache Mahout]

Apache Mahout is a project of Apache Software Foundation. Mahout helps building scalable Machine Learning applications. It primarily focuses in the areas of Collaborative Filtering, Classification, and Clustering.
Here is a very nice video tutorial on Mahout Item Recommender Tutorial using Java and Eclipse. It thoroughly explains about how to use Movielens dataset and create an Item-based recommender system to recommend certain number of most similar items for each items.

Python, Recommender System »

[7 Mar 2016 | 3 Comments]

Crab as known as scikits.recommender is a Python framework for building recommender engines integrated with the world of scientific Python packages (numpy, scipy, matplotlib).
Currently, Crab supports two Recommender Algorithms: User-based Collaborative Filtering and Item-based Collaborative Filtering.