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Recommendation system construction method and device

A recommender system and construction method technology, applied in the field of recommender system construction methods and devices, can solve problems such as difficulty in meeting privacy protection requirements, data batch leakage, difficulty in meeting big data model training, etc., so as to improve privacy and security, Solve the dependency problem of processing performance and improve the effect of training efficiency

Inactive Publication Date: 2018-09-11
ALIBABA GRP HLDG LTD
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AI Technical Summary

Problems solved by technology

[0003] On the one hand, the traditional data model training scheme has been difficult to meet the needs of privacy protection: first, the user's private data needs to be uploaded to the server for centralized processing, which is already very sensitive for some users
Even if the server will not take the initiative to abuse or leak user privacy, there are still hidden dangers that the server will be attacked and cause data leakage in batches
[0004] On the other hand, model training based on big data has a huge demand for storage and computing resources, and with the promotion of machine learning technology, the amount of data used in training and the complexity of the model are in a continuous upward trend. Increasing the performance or number of servers has become increasingly difficult to meet the needs of big data model training

Method used

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  • Recommendation system construction method and device
  • Recommendation system construction method and device

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Embodiment Construction

[0032] In order for those skilled in the art to better understand the technical solutions in the embodiments of this specification, the technical solutions in the embodiments of this specification will be described in detail below in conjunction with the drawings in the embodiments of this specification. Obviously, the described implementation Examples are only some of the embodiments in this specification, not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this specification shall fall within the scope of protection.

[0033] The function of the recommender system (Recommend System, RS) can be described as: recommend items (Item) for users (User) that meet their needs. The "item" here is a broad definition. In different application scenarios, it may correspond to different The actual meaning of , for example, an Item can represent a product, a piece of news, a song, a restaurant, or a user, etc. In the recommen...

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Abstract

Disclosed are a recommendation system construction method and device, being applied to a system comprising a plurality of users, the scoring information of objects of the users, the user preference vectors of the users, and the object feature vectors of the users are stored at the user terminals of any user, and the plurality of user terminals are trained in a collaborative mode to realize matrixdecomposition.

Description

technical field [0001] The embodiments of this specification relate to the technical field of big data processing, and in particular to a method and device for constructing a recommendation system. Background technique [0002] In the era of big data, by mining massive data, training samples can be formed, and various forms of data models can be further trained. In the prior art, a common implementation solution is to centrally store training samples on the server side and perform model training on the server side. However, this centralized training method has at least the following disadvantages: [0003] On the one hand, the traditional data model training scheme has been difficult to meet the needs of privacy protection: first, the user's private data needs to be uploaded to the server for centralized processing, which is already very sensitive to some users. Even if the server does not take the initiative to abuse or disclose user privacy, there is still a hidden dange...

Claims

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Application Information

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IPC IPC(8): G06N99/00G06F17/30G06F21/62G06Q30/06
CPCG06F21/6245G06Q30/0631G06Q30/02G06Q30/0269G06N5/022G06N20/00G06N5/04
Inventor 陈超超周俊
Owner ALIBABA GRP HLDG LTD
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