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An open source recommender system service written in Go
An open-source recommender system service written in Go. Recommend items from Popular, latest, user-based, item-based and collaborative filtering. Search the best recommendation model automatically in the background. Support horizontal scaling in the recommendation stage after single node training. Support Redis, MySQL, Postgres, MongoDB, and ClickHouse as its storage backend.
...You can even combine both approaches efficiently in the same query, something no other engine can do. Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information.
IRSA is a toolkit for Information Retrieval Service Assessment.
...It builds upon the Grails Web Framework and is developed at GESIS.
It implements two main functionalities: (1) A number of showcases that show the implemented services like a so-called Search Term Recommender and different science-model based ranking mechanisms and (2) an IR assessment module that let's one do an interactive evaluation of the retrieval services.
All implemented services are available via well-documented RESTful API. This toolkit is distributed under an Apache License 2.0.
The Idealize Recommendation Framework supports the development of recommender systems. We have devised a set of requirements all recommender systems must address, an architecture for those requirements, a technical vocabulary and a production model.
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The Duine Framework allows one to develop prediction engines for recommender systems. It contains a set of prediction techniques, a way to combine these techniques and a profile manager. The framework has a plug-in architecture, allowing customization.