Now, more young consumers are more inclined to choose their favorite music through streaming media services. In addition, this service generates a large amount of heterogeneous music data, which is far above the capacity limit of young consumers [1]. Therefore, the customized and personalized music recommendation mode can prevent users from wasting time on the music they do not like,

And directly lock in the type of music you like. It can be seen that music recommendation based on consumers' preferred mode is the trend and direction of future development. Many preference recommendation methods only care about consumers' past preferences for music but do not pay enough attention to short-term priorities. Appreciation of music is relevant over a certain period. For example, people generally prefer dynamic and fast-paced music when running outdoors or exercising in the gym; when reading or shopping, people typically listen to relaxing, slow-paced music. With the “popularization” of electronic products, people are no longer bound by time and place to listen to music. However, it is not easy to obtain consumer consumption information directly and quickly. The use of “session records” can help solve such problems. The so-called session record refers to the general term for the interactive behaviors generated by consumers within a certain period, such as shopping carts.

The server establishes different session records for different groups in a certain period to distinguish other consumer groups and track the click records of consumers. The server is called a related record sequence. However, the server cannot obtain session records due to legal issues. This paper divides the historical records into different session records by simple methods such as data collection and processing [2]. The core is that timestamps are separated by more than half an hour of time Δt, defined as different session records. This article simulates this session recording to meet the relevant requirements.

This paper mainly uses the algorithm, and the negative sampling accelerated training model to learn the correlation vector, which can grasp the actual situation of music in the big data environment based on session recording [3]. The weighted value of the previous word vectors of the session record can indicate the most relevant music that the consumer has obtained at present. In the last step, this paper conducts an empirical study. After being segmented by sessions, the FM dataset shows that the algorithm is correct. The empirical conclusion shows that the algorithm proposed in this paper will have a better effect than the traditional algorithm in the past. This indicates that the psychology of consumers in short-term conversations will be influenced by other factors.

Music recommendation based on consumers' session records is currently given the following definition: _{1},_{2},⋯,_{n}_{1},_{2},⋯,_{m}_{u}

The Continuous Bag of Words (CBOW) model can help consumers achieve this function: the music they enjoy now is highly correlated with the earlier piece. With the help of learning core words and the content of the sequence of user conversation records [5], this paper obtains the distribution vector representation of music.

This paper understands a conversation record as a paragraph when dealing with logically distributed text that conforms to a transition probability function. This article compares this type of music with all the words in a passage. This paper derives the session sequence. The music corresponds to all timestamps in the session record, and the background window size is set to. At the same time, according to the CBOW model, the so-called objective function is established ^{1},^{2},⋯,^{T}^{t}_{i}^{d}_{i}^{d}

And represent the indices in the music dictionary as vectors of background words and center words, respectively. The entry index of this dictionary is. The index of the background word in the dictionary is. Given the background word, the conditional probability of generating the center word is: The index of the headword _{c}_{o}_{1},⋯,_{o}_{2w} in the dictionary is _{o}_{1},⋯,_{o}_{2w}. The conditional probability of generating a center word given a background word is:

The operation of softmax depends on the size of the music dictionary. Musical scales are often in the millions, and all steps of the gradient computation contain the terms of the dictionary scale. It's computationally staggering. This paper uses negative sampling for similar training [7]. In this paper, the binary activation function sigmoid is used to represent the accurate center word, and neg(m) is used to represent the noise word. There is only one central word in positive examples, while in negative examples, the main word appears according to the noise word distribution function P(m). Generate the remaining neg(m) noncentral words in a random manner. The more frequently a word occurs, the more likely it is to be selected as a noise word, which is sampled. In this binary classifier, when a background word matches a positive example (center word), then D(p)=1, and when a background word reaches a negative example (noise word), it will be considered a negative Sample D(p)=0. The conditional probability is rough as follows:

The computational cost is reduced from _{o}_{1} +⋯+ _{o}_{2w})/2

Then this paper iterates over the parameters. The weighted iterative formula for noise words is as follows _{p}

The weight update formula for background words is as follows:

In this paper, different biased logistic distribution regression models are established. We assume that
_{1},_{2},⋯_{q}^{T} is the location parameter vector of the scale model of _{i}_{i}_{i}

We use Newton's iteration method to implement the calculation and estimate the parameters. The algorithm is as follows:

Step 1: Select the initial value

Step 2: Implement iteration ^{(k+1)} = ^{(k)} − ^{−1}(^{(k)})^{(k)}) given the current value.

Step 3: Continue to perform the second step and stop when the convergence condition is reached.

To generate simulated data from the model, the paper uses the following three mean squared errors to assess the quality of the estimates:

The likelihood function of a class of limited logistic distribution is:

The log-likelihood function is:

The second-class partial likelihood function is:

Then the log-likelihood function is:

The empirical research in this paper uses the NetEase Cloud Music dataset. The dataset includes a massive collection of hearing records from consumers, including four. The details are shown in Table 1. which contains the <

NetEase Cloud Music Dataset

Parameter | Scale |
---|---|

User | 992 |

Listen to the record | 19150868 |

Artist | 176948 |

Music | 1500661 |

The experiments defined the time stamp interval Δt over 30 minutes (1800 seconds) as adjacent session recordings [9]. The empirical research in this paper is done on a PC, and the operating system is Ubuntu 16.04 installed.

Experiment 1: Word Embedding Models Evaluate F-Measures under Different Window Sizes and Dimensions. This article uses the Top-K recommendation method. In this empirical study, this paper selects the value of K as five [10]. We empirically investigate the correlation of the window size variables w and F measurements. The fixed size is 100. The window size variables w are 2, 4, 6, 8, and 10, respectively. The horizontal axis of Figure 1 represents the window value. The vertical axis represents measurements.

In the case where the window data is small, the number of songs is minimal, and vice versa. In a session, the longer the interval between pieces of music, the smaller the correlation [11]. Although the session record has been processed, the window value still has a significant effect on the recommendation algorithm.

The correlation between the projection layer size data N and the F measurement value is empirically studied. Set the window size to 6. The values of the projection layer size data N are 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, and 300, respectively. The horizontal axis of Fig. 2 represents dimension values, and the vertical axis represents measurement values and the vertical axis represents the _{1} measure.

When the dimension is smaller, the word vector cannot contain all the information of the song. When the paper gradually increases the measurement value, the accuracy of the recommendation continues to improve, but when the range exceeds 150, the accuracy decreases significantly. However, when the dimension selection exceeds a certain threshold, there will be a decline [12]. In the case of representing song attributes with neurons, it does significantly increase the computational complexity of the model. In this paper, multiple pieces of music are arbitrarily taken, and the vector is reduced to two dimensions by t-SNE. The effect diagram is shown in Figure 3. It can be seen that the related music has a very high correlation in the two-dimensional space.

The smaller the spatial distribution interval of a certain musician's work. Check the last one. FM tag information, it is judged that the songs have the same tag, including stoner rock, alternative rock, fip, etc. Not only that, with the help of BPM to simulate the rhythm of all songs, and study its rate, we can see that the BPM values are 134, 176, 162 and 85. They are all classified as relatively smooth rhythm works. This data suggests that similarities between songs can also emerge in rhythm.

Experiment 2: The K value of the sequence length is taken as 5, 10, 15 and 20 respectively, and the goal is to verify the usability and effectiveness of the related algorithm.

Session pop refers to recommending some of the most popular and currently most popular items to consumers.

DCF refers to recommending items to consumers by assessing the correlation between the items consumers clicked on before and according to the consumers' past behavior. The method initially estimates the similarity between items. Second, a list of recommended items is generated based on the distance and the user's historical behavior.

The Bayesian ranking simulation algorithm (BPR-MF) combines the two algorithms of BPR and matrix factorization. It contains the properties of the user or item. This algorithm is very helpful for cold start problems.

Figure 4 shows that the session logistic distribution proposed in this paper is helpful in fitting the transition probability function. When the value of the recommendation list increases, the utility value increases accordingly, and then becomes flat [13]. When the value of this item is taken as 5, it is less likely to find items that consumers like. vice versa.

A music recommendation algorithm based on logistic distribution fitting transition probability function is proposed, which is very helpful for studying short-term behavior correlation. At the same time, the algorithm is used in many scenarios, and it is highly effective for models with features such as region, gender, and age.

#### NetEase Cloud Music Dataset

Parameter | Scale |
---|---|

User | 992 |

Listen to the record | 19150868 |

Artist | 176948 |

Music | 1500661 |

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