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An improved content-based TV music recommendation method

A recommendation method and music technology, applied in the fields of electrical digital data processing, instruments, calculations, etc., can solve the problem of not being able to broaden the dimension of interest, and achieve the effect of reducing the amount of data calculation, improving comfort, and broadening the coverage dimension

Active Publication Date: 2022-05-17
SICHUAN CHANGHONG ELECTRIC CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this algorithm can only recommend items contained in the dimension of interest represented by the user’s historical behavior data for the user, but cannot broaden the dimension of interest for the user based on the current interest, and recommend items of different dimensions

Method used

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  • An improved content-based TV music recommendation method
  • An improved content-based TV music recommendation method
  • An improved content-based TV music recommendation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] Such as figure 1 , figure 2 As shown, an improved content-based TV music recommendation method, including:

[0032] S1. Obtain the dimensions in the user's historical data information; the dimensions described in the present invention can be understood as types of data information.

[0033] S2. Obtain the user collection corresponding to each dimension;

[0034] S3. Calculate the corresponding Jaccard similarity between each dimension according to the user collection; the calculation formula for calculating the Jaccard similarity is J(A, B)=(A∩B) / (A∪B), where A and B respectively represent the corresponding user collection in one dimension.

[0035] S4. Perform interest scoring on dimensions;

[0036] S5. Obtain the data information under each dimension and sort according to the degree of interest of all users;

[0037] S6. Select the top X dimension of dimension interest score and the corresponding top Y dimension of Jaccard similarity to form a dimension set;

...

Embodiment 2

[0046] On the basis of Example 1, such as image 3 As shown, in this embodiment, a specific music recommendation is taken as an example for description. An improved content-based TV music recommendation method, including:

[0047] S11. Sorting the user's historical data information by interest degree; extracting the music data listened to by the user in the past three months, and according to the existing data, the frequency of listening to music and the percentage of listening time of the song are used for weighted summation as the user's rating of the song. Sort by rating.

[0048] S12. Extract the dimensions of all the interest information of the top W; extract all the interest dimensions of the top 20 songs scored by users, the complete set of interest dimensions are: pop, rock, hip-hop, light music, jazz, classical, folk, electronic, R&B, blues, Country, folk songs.

[0049] S2. Obtain the collection of users corresponding to each dimension; extract the collection of l...

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Abstract

The present invention relates to the technical field of personalized recommendation. In order to solve the problem that the traditional content-based algorithm cannot expand and recommend the dimension of interest, it is specifically an improved content-based TV music recommendation method, including: S1, obtaining user historical data Dimensions in the information; S2. Obtain the user collection corresponding to each dimension; S3. Calculate the corresponding Jaccard similarity between each dimension according to the user collection; S4. Score the interest of the dimension; S5. Obtain the user collection under each dimension The data information is sorted according to the interest degree of all users; S6, select the top X dimension of the dimension interest score and the corresponding top Y dimension of the Jaccard similarity to form a dimension set; S7, select each dimension in the dimension set The top Z data information forms the candidate set M; S8, adopts the content-based algorithm to obtain the candidate set N for the user historical information; S9, merges the candidate sets M and N to obtain the recommended set U. The above method can broaden the dimension of personalized recommendation and the diversity of recommendation.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to an improved content-based TV music recommendation method. Background technique [0002] The principle of the content-based algorithm is to obtain the user's interest preference according to the user's historical behavior, and recommend items similar to his interest preference for the user. The implementation steps are as follows: Mining the user's basic information and historical behavior data, constructing User characteristics; mining the basic information of items to construct item content information features; recommending to users based on the similarity between user features and item content features. This algorithm is often used when the recommendation system has a certain amount of user data after the cold start phase. However, this algorithm can only recommend items included in the dimension of interest represented by the user's historical behavior da...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/635
CPCG06F16/635
Inventor 何林凯
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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