Music recommendation method and apparatus

• US 8,442,925 B2
• Filed: 04/18/2011
• Issued: 05/14/2013
• Est. Priority Date: 11/30/2010
• Status: Expired due to Fees

• Current Assignee: Beijing Ruixin Online System Technology Co., Ltd.
• Original Assignee: Beijing Ruixin Online System Technology Co., Ltd.
• Inventors: Wang, Paul
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Tran, Mai T
• Application Number:
  US13/088,484
• Publication Number:
  US 20120136814A1
• Time in Patent Office: 757 Days
• Field of Search: 706/12, 707/732, 707/748
• US Class Current: 706/12
• CPC Class Codes: G06F 16/60 of audio data
• CPC Class Codes: G06F 16/65 Clustering; Classification

Abstract

A music recommendation method may include obtaining the music belongingness function of music, which is the set of granularity of music in different dimensions, wherein the dimension is the classification of music and the granularity is the classification of the dimension; obtaining the user belongingness function of a user, which is the set of granularity indicating likes of user in different dimensions; calculating a granularity correlation function by using the music belongingness function and the user belongingness function; calculating the value of the probability function indicating likes of user for music by using the granularity correlation function and a dimension weighting coefficient; and recommending the music to the user when the value of the probability function indicating likes of user for music is greater than a preset threshold. An apparatus applying to the method comprises corresponding modules.

Claims

1. A music recommendation method for use with a music recommendation apparatus, the method comprising:

obtaining a music belongingness function of a first piece of music, the music belongingness function of music comprising a first set granularities of music in different dimensions, wherein the dimensions are classifications of music and the granularities are classifications of the dimensions, and an expression of the music belongingness function being A(music, P_k)={p_kj|j=1, 2, . . . , n}, k=1, 2, . . . m, wherein P_k represents a dimension, p_kj represents a granularity, m represents a total number of the dimension and n represents a total number of the granularity in the dimension;

obtaining a user belongingness function of a user, the user belongingness function comprising a second set of granularities indicating user music tastes in the different dimensions, and an expression of the user belongingness function being A(user, P_k)={p_ki|i=1, 2 . . . , n}, k=1, 2, . . . m, wherein P_k represents a dimension, p_ki is a granularity, m represents a total number of the dimension and n represents a total number of the granularity in the dimension;

calculating a granularity correlation function by using the music belongingness function and the user belongingness function, and an expression of the granularity correlation function being

2. The music recommendation method according to claim 1, wherein the obtaining the music belongingness function of the first piece of music comprises obtaining the music belongingness function of the first piece of music by one of the following ways:

receiving manual input and performing program identification.

3. The music recommendation method according to claim 1, wherein at least one user belongingness function is assigned to the user.

4. A computerized music recommendation apparatus comprising:

a music belongingness function obtaining unit for obtaining a music belongingness function of a first piece of music, the music belongingness function comprising a first set of granularities of music in different dimensions, wherein the dimensions are classifications of music and the granularities are classifications of the dimensions, and an expression of the music belongingness function being A(music, P_k)={p_kj|j=1, 2, . . . , n}, k=1, 2, . . . m, wherein P_k represents a dimension, p_kj represents a granularity, m represents a total number of the dimension and n represents a total number of the granularities in the dimension;

a user belongingness function obtaining unit for obtaining a user belongingness function of a user, the user belongingness function comprising a second set of granularities indicating user music tastes in the different dimensions, and an expression of the user belongingness function being A(user, P_k)={p_ki|i=1, 2 . . . , n}, k=1, 2, . . . m, wherein P_k represents dimension, p_ki represents a granularity, m represents a total number of the dimensions and n represents a total number of the granularity in the dimension;

a granularity correlation function calculating unit for calculating a granularity correlation function by using the music belongingness function and the user belongingness function, and an expression of the granularity correlation function being

5. The music recommendation apparatus according to claim 4, wherein the music belongingness function obtaining unit obtains the music belongingness function of the first piece of music by one of the following ways:

receiving manual input and performing program identification.

6. The music recommendation apparatus according to claim 4, wherein at least one user belongingness function is assigned to the user.