Methods and systems for pushing audiovisual playlist based on text-attentional convolutional neural network

Patent metadata

Field	Value
Publication number	US-11580979-B2
Application number	US-202117141592-A
Country	US
Kind code	B2
Filing date	Jan 5, 2021
Priority date	May 7, 2020
Publication date	Feb 14, 2023
Grant date	Feb 14, 2023

Abstract

In some embodiments, methods and systems for pushing audiovisual playlists based on a text-attentional convolutional neural network include a local voice interactive terminal, a dialog system server and a playlist recommendation engine, where the dialog system server and the playlist recommendation engine are respectively connected to the local voice interactive terminal. In some embodiments, the local voice interactive terminal includes a microphone array, a host computer connected to the microphone array, and a voice synthesis chip board connected to the microphone array. In some embodiments, the playlist recommendation engine obtains rating data based on a rating predictor constructed by the neural network; the host computer parses the data into recommended playlist information; and the voice terminal synthesizes the results and pushes them to a user in the form of voice.

First claim

What is claimed is:

1. A method for pushing an audiovisual playlist based on a text-attentional convolutional neural network comprising:
(A) constructing a user information database and an audiovisual information database;
(B) processing an audiovisual introduction text in said audiovisual information database comprising
(i) using a text digitization technique to obtain a fully digital structured data;
(ii) using said fully digital structured data as an input into said text-attentional convolutional neural network, and
(iii) calculating a hidden feature of said audiovisual introduction text by a first equation:

{

z
w

=

tan
⁢

h
(

WX
w

+
p

)

,

y
w

=

K
⁢

z
w

+
q

,

wherein, W is a feature extraction weight coefficient of an input layer of said text-attentional convolutional neural network;
K is a feature extraction weight coefficient of a hidden layer; W∈R n h ×(n−1)m ; p∈R n h ; K∈R n h ×N ; q∈R N ; and
a projection layer X w is a vector composed of n−1 word vectors of the input layer, with a length of (n−1)m ;
(iv) calculating y w ={y w,1 , y w,2 , . . . , y w,N }, and letting W i represent a word in a corpus Context(w i ) composed of said audiovisual introduction text, and normalizing by a softmax function to obtain a similarity probability of word w i in a user rating of a movie:

p
⁡
(

w
⁢

❘
"\[LeftBracketingBar]"

Context
(
w
)

)

=

e

y

w
,

i
w

∑

i
=
1

N

e

y

w
,
i

wherein, i w represents an index of word w in said corpus Context(w i )
y w,j w represents a probability that word w is indexed as i w in said corpus Context(w i ) when said corpus is Context(w);
(v) letting said hidden feature of said audiovisual introduction text be F in an entire convolution process, F={F 1 , F 2 , . . . , F D }, and letting F j be a jth hidden feature of said audiovisual introduction text, then: F j =text_cnn(W,X)
wherein, W is the feature extraction weight coefficient of the input layer of said text-attentional convolutional neural network; X is a probability matrix after digitization of the audiovisual introduction text;

(C) extracting a rating feature of probability matrix X by a convolutional layer of said text-attentional convolutional neural network; setting a size of a convolution window to D×L; amplifying and extracting, by a max-pooling layer, a feature processed by the convolutional layer and affecting a user's rating into several feature maps, that is, using N one- dimensional (1D) vectors H N as an input in a fully connected layer; and
mapping, by the fully connected layer and an output layer, a 1D digital vector representing main feature information of a movie into a D-dimensional hidden feature matrix V of movies about user rating;
(D) counting historical initial rating information of users from an open dataset Movielens 1 m, and obtaining a digital rating matrix of [0,5] according to a normalization function, wherein N represents a user set; M represents a movie set; R ij represents a rating matrix of user u i about movie m j; R=[R ij ] m×n represents an overall initial rating matrix of users;

decomposing R into a hidden feature matrix U∈R D×N of user rating and a hidden feature matrix V∈R D×N of movies; then, calculating a user similarity uSim(u i ,u j ), and classifying a user with a similarity greater than 0.75 as a neighboring user;

uSi
⁢

m
⁡
(

u
i

,

u
j

)

=

∑

m
∈

R
M

(

r

u
i

,
m

-

r
m

Assignees

• Univ Chongqing

Inventors

• Song Yongduan
• Lai Junfeng
• Zhou Xin

Classifications

• Y02D10/00

  Energy efficient computing, e.g. low power processors, power management or thermal management · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• H04R3/005

  for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407) · CPC title

• G06N3/045

  Combinations of networks · CPC title