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Recommendation method and system

A recommendation method and recommendation system technology, applied in the field of information processing, can solve problems such as uncontrollable computational complexity, poor effect, high computational complexity, etc.

Active Publication Date: 2014-09-24
百度移信网络技术(北京)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, the insurmountable high computational complexity
[0004] However, there are various problems in previous recommendation methods. For example, user_based CF and item_based CF are the same algorithm; user_based CF and item_based CF have poor recommendation effects; existing similarity calculation methods are mainly based on attribute vectors. The similarity calculation method has a poor effect; the matrix decomposition algorithm has a poor effect when adapting to implicit feedback information; the problem of uncontrollable computational complexity; the best solution to problems such as matrix sparsity and cold start; based on Information on user behavior and information based on content, tags, social relations, etc., which cannot be effectively integrated and maximized

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] First take the continuous case as an example. For continuous cases, the given weights are all 1. Examples of book recommendations in the online bookstore, reference figure 1 Explain the similarity measurement method. First, as shown in step S1, the server collects all user information and all book information in the online bookstore, as well as all historical data of the user clicking and reading the book. Set the collection of all books in the online bookstore to set M(m1, m2,...), and set the set of all users to set N(n1, n2,...), assuming that it is in set M The attribute values ​​of the elements in the sum set N satisfy a uniform distribution from positive infinity to negative infinity. Below we introduce how to obtain the similarity between users based on the historical data of the user's operations on the books without knowing any attribute information of the book or the user's attribute information.

[0063] Now suppose that the book that user n1 wants to see in ...

Embodiment 2

[0083] Taking the calculation of the similarity between the user and the user, or the item and the item in order to recommend items to the user in online shopping as an example, the comparison object here is the user and the user, or the item and the item. reference figure 2 The following description is given. First, like figure 2 As shown in step S21, the server collects information based on the user's login and registration, the items sold on the website, and the user's operation of the items, that is, the collected information includes the user, the item, and the interaction between the user and the item , To obtain data about users, items, and user operations on items. The server analyzes the above information, one is the user collection User, the other is the item collection Item, and the user's operation records on the items. Here, each user's operation on the item is independent of each other, and each operation expresses the same meaning, which indicates that the use...

Embodiment 3

[0119] Embodiment 3 is to perform an operation to enhance similarity correlation on the result obtained in Embodiment 1. We know that the larger the variance, the more the results of the association, but the error also increases.

[0120] Figure 4 Shows a flowchart showing the method for enhancing similarity association of Embodiment 1, refer to Figure 4 Example 3 will be described. Using the above-mentioned similarity to define Equation 1, and according to the similarity results obtained in Example 1, in Figure 4 Step S41 is passed to any book m x And m y , And m y And m z The similarity of m y The convolution operation of, as shown in Equation 9, can get m x And m z Therefore, the range of similarity between books is expanded, the similarity between books is enhanced, and the similarity is enhanced sim(m x , M y ). Through the calculation of Equation 9, the variance that satisfies Equation 1 also becomes 4δ 2 .

[0121] sim ( m x , m z ) = ∫ - ...

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Abstract

The invention relates to a recommendation method and system. The recommendation method comprises the following steps: a data acquisition step: acquiring behavior data about a user and feature data of objects; a similarity calculating step: utilizing the acquired behavior data and feature data to acquire a similarity matrix between the objects; a recommendation-matrix calculating step: utilizing the behavior data of the user to generate a probability matrix of the user for the objects and multiplying the probability matrix and the similarity matrix to acquire a recommendation matrix.

Description

Technical field [0001] The present invention relates to the field of information processing, in particular to a method and system for similarity measurement in the field of information processing. Background technique [0002] Currently, similarity measurement is involved in many fields, and similarity analysis is performed based on various existing similarity measurement methods to make recommendations. For example, recommended methods involved in the Internet industry and other fields. [0003] The existing methods for making recommendations include the following. One is based on system filtering technology to generate recommendations. Including the use of existing similarity measurement methods. Such as: Pearson correlation coefficient, Jaccard coefficient, cosine similarity, correlation similarity and other similarity measurement methods to obtain the nearest neighbors of TOP_N. Use TOP_N's neighbors to recommend relevant results based on its operation. One is to generate ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 朱宝
Owner 百度移信网络技术(北京)有限公司