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 … Read more
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. Here is a QuickStart tutorial on using python-recsys for Recommender Systems. It takes movielens’s movie ratings dataset and shows examples about computing similarity between movie items and recommending movies to users. Here, … Read more
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. Here are some useful resources for LibRec: LibRec Tutorial LibRec Examples on Real Data Sets & comparison with other recommendation libraries A Collection of Recommendation Data … Read more
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 … Read more
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. Here is a tutorial on Introduction to Recommender Systems with Crab. It briefly explains about what Recommendation is, … Read more