Difference between revisions of "RecDB"
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'''RecDB''' is An Open Source Recommendation Engine Built Entirely Inside PostgreSQL 9.2. RecDB allows application developers to build recommendation applications in a heartbeat through a wide variety of built-in recommendation algorithms like user-user collaborative filtering, item-item collaborative filtering, singular value decomposition. Applications powered by RecDB can produce online and flexible personalized recommendations to end-users. | '''RecDB''' is An Open Source Recommendation Engine Built Entirely Inside PostgreSQL 9.2. RecDB allows application developers to build recommendation applications in a heartbeat through a wide variety of built-in recommendation algorithms like user-user collaborative filtering, item-item collaborative filtering, singular value decomposition. Applications powered by RecDB can produce online and flexible personalized recommendations to end-users. | ||
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[[Category: Database]] | [[Category: Database]] | ||
[[Category: Software]] | [[Category: Software]] | ||
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| + | [https://github.com/Sarwat/recdb-postgresql] | ||
| + | [https://github.com/Sarwat/recdb-postgresql] | ||
Revision as of 18:10, 12 December 2013
link title RecDB is An Open Source Recommendation Engine Built Entirely Inside PostgreSQL 9.2. RecDB allows application developers to build recommendation applications in a heartbeat through a wide variety of built-in recommendation algorithms like user-user collaborative filtering, item-item collaborative filtering, singular value decomposition. Applications powered by RecDB can produce online and flexible personalized recommendations to end-users.
RecDB is an out-of-the-box tool for web and mobile developers to implement a myriad of recommendation applications. The system is easily used and configured so that a novice developer can define a variety of recommenders that fits the application needs in few lines of SQL. Crafted inside PostgreSQL database engine, RecDB is able to seamlessly integrate the recommendation functionality with traditional database operations, i.e., SELECT, PROJECT, JOIN, in the query pipeline to execute ad-hoc recommendation queries. The system optimizes incoming recommendation queries (written in SQL) and hence provides near real-time personalized recommendation to a high number of end-users who expressed their opinions over a large pool of items.
