Collaborative filtering method based on domain correlation self-adaption

Abstract

A collaborative filtering method based on domain correlation self-adaption comprises the following steps that 1,the difference of an auxiliary domain and a target domain serves as a regularization term and is introduced into a traditional model to obtain a new model (shown in the description), wherein T is a rating matrix having a partially deleted item in the target domain, Z and T have the same rating item; the equation shown in the description represents an index set representing the target domain, wherein the equation is shown in the description; | | . | | F represents a Frobenius norm, wherein the equation is shown in the description; | | . | | * represents a nuclear norm,| | Z | | *represents the sum of all singular values of a matrix Z; gamma is a regularization parameter, eta < (0, 1) represents the similarity of the auxiliary domain and the target domain; 2,a regular optimization solution Z* = Z of the new model is calculated by using a fixed-point iteration algorithm. The collaborative filtering method can be applied to an Internet recommendation system, introduces the system to a prediction model of the target domain through self-adaptive estimation of their correlation, accordingly achieves knowledge migration effectively and improves the recommendation precision of the target domain.

Description

technical field [0001] The invention relates to the field of data mining direction in information processing technology, in particular to a collaborative filtering method based on field correlation self-adaptation, which can be applied to an Internet recommendation system. Background technique [0002] With the increasing development of information and Internet of Things technology, the Internet meets the needs of users for information in the information age, brings convenience to people, and also brings a lot of information processing problems. When faced with massive amounts of information, users cannot obtain the part of information that is really useful to them, which makes the use of big data less efficient, and the so-called information overload problem arises. A very promising solution to the problem of information overload is the recommendation system. Recommender systems play an important role in many applications, for example, in e-commerce fields like Amazon, Tao...

AI Technical Summary

Problems solved by technology

The debugging of parameters brings great difficulties to the application of the algorithm in practical problems

[0006] (2) Higher data requirements for auxiliary fields

These requirements on auxiliary domains greatly reduce the practicality of transfer learning methods

[0007] (3) For large-scale data, some migration learning algorithms require too much calculation

In practical applications, it usually involves processing massive users' ratings on items, and the excessive calculation required by the algorithm directly limits its practicability

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