Recommendation method and system for local low rank matrix approximation based on implicit feedback information

Abstract

The invention discloses a recommendation method and system for local low rank matrix approximation based on implicit feedback information, and belongs to the technical field of recommendation. The invention starts from the implicit feedback information that is ignored in the user information, uses an implicit feedback information optimization feature extraction model to extract the single-dimensional multi-dimensional preference vectors of a user and an item according to the local low rank matrix approximation principle, and gives a more accurate user rating recommendation manner on the basisof preference information of the user and the item. The recommendation system comprises a data engine module, a multi-dimensional feature extraction module, a score prediction module, a collaborativerecommendation module and an information update module; through the collaborative work of all the modules, the multi-dimensional recessive factors of the user in different environments are extracted,and the accuracy and personalization of the whole system recommendation result is improved; the universality is high; and the method and the system are suitable for most of the existing recommended scenarios.

Description

technical field [0001] The invention belongs to the field of recommendation technology, and more specifically, relates to a recommendation technology based on local low-rank matrix approximation based on implicit feedback information. Background technique [0002] The development of Internet technology has brought us many conveniences, but it has also brought us many difficulties. The most famous of these is the “information overload” problem. Due to the convenience of the Internet, all our information is interacted on the Internet, and all data are stored in cloud databases, resulting in a geometric multiple of network data, which makes it difficult for people to find the information they need on the Internet. [0003] The search engine is an important invention, which allows people to retrieve matching information based on keywords from massive data, and alleviates the pressure on users caused by excessive data volume to a certain extent. However, it ignores that differe...

AI Technical Summary

Problems solved by technology

If these hidden factors are ignored only based on the analysis of the user's historical data, it is likely that the change speed of the recommendation results will be slower than the change speed of the user's preferences, resulting in a lag in the recommendation results; Preferences, as ambient noise, processed, ignored

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