Technologies for recommender systems Sample Clauses

Technologies for recommender systems. This section describes a tentative list of available packages and tools that could be used to develop the personal recommendations for stakeholders in OpenReq: ● Apache Mahout16 (Apache License v2). This Java library supports a lot of collaborative filtering algorithms. It is highly scalable, but content-based recommendation is not supported by the library. The library is characterized by having a good documentation, high performance, and maintainability. ● Apache PredictionIO17 (Apache License v2). It is a very active open source project that provides a machine learning server that can be used to create a recommender system. It supports both content-based and collaborative filtering. Additionally, it has a general interface to develop other types of recommenders (such as knowledge-based). ● LibRec18 (GNU General Public License). LibRec is a Java based recommendation engine with more than 70 kinds of recommendation algorithms. Specifically, these algorithms can be divided into benchmark algorithms, collaborative filtering algorithms, content-based algorithms, context-aware algorithms, hybrid algorithms and other extended algorithms. ● LightFM19 (Apache License v2). It is an actively-developed Python implementation of a number of collaborative- and content-based learning-to-rank recommender algorithms. It easily scales up to very large datasets on multi-core machines. ● MyMediaLite20 (GNU General Public License). MyMediaLite is, comparable to Apache Mahout, a recommendation library for collaborative filtering that runs on .NET. It contains not that much algorithms than Apache Mahout but it supports two commonly occurring collaborative filtering scenarios: 1) rating prediction (e.g., on a scale of 1 to 5 stars), and 2) item prediction from positive-only feedback (e.g., from clicks, likes, or purchase actions). ● RankSys21 (Mozilla Public License v2). It is Java recommendation system that includes support for the evaluation and enhancement of novelty and diversity.
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