Recommender alternatives and similar libraries
Based on the "Machine Learning" category.
Alternatively, view Recommender alternatives based on common mentions on social networks and blogs.
-
xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow -
mxnet
DISCONTINUED. Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more -
Caffe2
DISCONTINUED. A lightweight, modular, and scalable deep learning framework. [Apache2] website -
vowpal_wabbit
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. -
catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. -
RNNLIB
RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/ -
OpenHotspot
DISCONTINUED. OpenHotspot is a machine learning, crime analysis framework written in C++11.
InfluxDB - Purpose built for real-time analytics at any scale.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Recommender or a related project?
README
Recommender
A C library for product recommendations/suggestions using collaborative filtering (CF).
Recommender analyzes the feedback of some users (implicit and explicit) and their preferences for some items. It learns patterns and predicts the most suitable products for a particular user.
Features
- Collaborative Filtering
- User and Item based recommenders
- No external dependencies
- Fast running time ~ 81 seconds for 10 million ratings (on MovieLens Data Sets)
- Memory footprint under 160 MB for 10 million ratings
Webpage
http://ghamrouni.github.com/Recommender/
Building
To compile Recommender:
make
The compilation will produce libRecommender.a
To compile an example:
gcc test/test.c src/libRecommender.a -lm -o test/t1 -I src/
Alternatively you can use clang
clang test/test.c src/libRecommender.a -lm -o test/t1 -I src/
Keywords
Collaborative filtering, recommender system
References
*Note that all licence references and agreements mentioned in the Recommender README section above
are relevant to that project's source code only.