# Rapid Classification and Analysis for E-Commerce Goods Based on Multitask Learning

Authors: Nana Liu
Journal: Security and Communication Networks
 (2021)

---

## Abstract

Today’s E-commerce is hot, while the categorization of goods cannot be handled better, especially to achieve the demand of multiple tasks. In this paper, we propose a multitask learning model based on a CNN in parallel with a BiLSTM optimized by an attention mechanism as a training network for E-commerce. The results showed that the fast classification task of E-commerce was performed using only 10% of the total number of products. The experimental results show that the accuracy of w-item2vec for product classification can be close to 50% with only 10% of the training data. Both models significantly outperform other models in terms of classification accuracy.


---

## Body

## 1. Introduction

With the rapid development of E-commerce economy, E-commerce platforms represented by Taobao and Jingdong are gradually becoming an indispensable part of people’s lives, and according to the 2016 China E-commerce Report, the total E-commerce transactions reached 26.1 trillion RMB in that year [1]. The traditional method of classifying products by manual labeling is no longer applicable, and an automated method is needed to effectively label product categories. A common labeling method is to use textual descriptions of products to classify them [2]. For example, a hierarchical classification model is proposed in [3], which first classifies goods at a coarse-grained level using a simple classifier and then classifies different types of goods in each broad category, while the method can automatically generate a hierarchical catalog tree.Wang et al. [4] argued that misclassification brings about a reduction in merchant sales profit and proposed catalog tree generation methods with the criterion of maximizing revenue loss. These methods are able to classify product categories more accurately when the textual corpus of the product is sufficient, but the accuracy is affected when textual information is missing or the textual description is inaccurate. The accuracy of this classification method relies heavily on the accuracy of the text description of the product and the method of generating features from the descriptors, so if the text description of the product is wrong or inappropriate or the method of generating features is inaccurate, the accuracy of the classification will be greatly affected. On the contrary, the rapid development of image processing technology in recent years has made it possible to combine the classification methods with image information [5–7]; however, due to the complexity of image processing and the diversity of product images, it is still a great challenge to extract efficient features from the images and classify the products with high accuracy.With the rapid increase in the number of users, E-commerce websites have gradually accumulated a huge amount of user behavior logs in their backend, which record when users operate on the website (click, purchase, etc.), and these structured user logs contain a variety of user behavior patterns and product characteristics [8].The contributions of this paper are as follows.We propose a multitask learning model that combines a CNN and a BiLSTM optimized by an attention mechanism in parallel as a training network and, finally, efficiently classify products into categories.We make a detailed definition of the scenario of users shopping on E-commerce shopping websites and browse and click on many goods. These browsing and clicking sequences are usually recorded as logs by the background of E-commerce websites to improve the user experience.We have done many experiments to verify the effectiveness of this scheme. From the accuracy of classification results, we compare the two models and three comparison methods. Next, we analyze and compare the parameter sensitivity of the two models.


## 2. Related Work

Models with the framework of multitask learning have been used in many fields, for example, in the field of NLP for lexical annotation and named body recognition [9]. In the field of evolutionary computation, it is investigated how to solve multiple optimization problems (tasks) using multitask learning to improve the efficiency of solving each task independently [10]. In the field of autonomous driving, it is proposed to use multiple related tasks to achieve accurate multisensor 3D target detection [11].Christopher et al. [12] proposed a word vector representation method word2vec, and the efficiency of training and the effectiveness of word representation in the field of natural language processing have been widely studied and discussed, while researchers in other fields have also tried to model and study domain-related problems using word2vec [13]. In social networks and graph-related studies, Laclavik et al. [14] considered nodes in graphs as words and sequences of nodes generated by random wandering as sentences in the natural language, based on which word2vec models are applied to represent nodes as vectors, which can be used for tasks such as association discovery and node relevance metrics in social networks with good results; Nargesi et al. [15] applied the embedding representation learning method to query rewriting in search engines and also achieved good results.


## 3. Problem Scenarios and Definitions

### 3.1. Problem Scenario

When users shop on E-commerce websites, they will browse and click on many products, and these browsing and clicking sequences are usually recorded as logs by the backend of E-commerce websites to improve users’ experience. Table1 shows an example of user behavior data logs recorded in the backend of Tmall website, which contains 5 fields, namely, user records, user ID, item records, product ID, category records, product category, action records, user action, and timestamp records, the time point of user action. The action field contains four different types of actions: click, collect, cart, and buy. These user behavior logs completely record the user’s operation on the website, which is important for user intention mining and product attribute research [16].Table 1
Example of log data.
UserCommodityCategoryOperationTimeu1aC1Click2013-06-02T16:20:00u1bC1Click2013-06-02T16:25:08u1aC1Buy2013-06-02T16:36:00u2cC2Click2013-06-01T12:20:00u2cC2Collect2013-06-01T12:28:30When users browse E-commerce websites, they usually do so with the purpose of buying some kind or class of goods, so the goods they operate within a certain time period have a higher probability of being the same or similar goods. In order to better utilize this property, firstly, the sequence of user operations is divided into different sessions, and in each session, the user has a consumption intention. For example, when a user wants to buy a cell phone, goods browsed or operated are different cell phone brands or other peripheral goods. In the principle of session division, this study adopts the time period division method commonly used in search engine research [17]. If the interval time between two consecutive operations is greater than a certain threshold, the user is divided into two different sessions. After the segmentation of sessions, the sequence of operations of users in different sessions is obtained, as shown in Table 2, where the first row represents a session of user u1, and the commodities operated in this session are a, b, a, a, and c. In the subsequent part, the sequence of operations of commodities in this session is directly represented by the notation of session, i.e., S1 = {a, b, a, a, c}.Table 2
Session example.
SessionUserCommodity sequenceS1u1a, b, a, a, cS2u2b, c, c, a, dS3u3m, n, n, c, nS4u4a, d, d, c, f, e


### 3.2. Problem Definition and Symbol Description

After sketching the problem scenario, the problem can be summarized as follows: mining the potential relationships between products through the user’s behavior logs, projecting the products into the feature space, and effectively clustering or classifying the products using their feature representations. In the subsequent model construction, one assumption is followed: most of the products operated by users in a session belong to one type [18]. This assumption is also illustrated and verified in the subsequent experimental section.


## 3.1. Problem Scenario

When users shop on E-commerce websites, they will browse and click on many products, and these browsing and clicking sequences are usually recorded as logs by the backend of E-commerce websites to improve users’ experience. Table1 shows an example of user behavior data logs recorded in the backend of Tmall website, which contains 5 fields, namely, user records, user ID, item records, product ID, category records, product category, action records, user action, and timestamp records, the time point of user action. The action field contains four different types of actions: click, collect, cart, and buy. These user behavior logs completely record the user’s operation on the website, which is important for user intention mining and product attribute research [16].Table 1
Example of log data.
UserCommodityCategoryOperationTimeu1aC1Click2013-06-02T16:20:00u1bC1Click2013-06-02T16:25:08u1aC1Buy2013-06-02T16:36:00u2cC2Click2013-06-01T12:20:00u2cC2Collect2013-06-01T12:28:30When users browse E-commerce websites, they usually do so with the purpose of buying some kind or class of goods, so the goods they operate within a certain time period have a higher probability of being the same or similar goods. In order to better utilize this property, firstly, the sequence of user operations is divided into different sessions, and in each session, the user has a consumption intention. For example, when a user wants to buy a cell phone, goods browsed or operated are different cell phone brands or other peripheral goods. In the principle of session division, this study adopts the time period division method commonly used in search engine research [17]. If the interval time between two consecutive operations is greater than a certain threshold, the user is divided into two different sessions. After the segmentation of sessions, the sequence of operations of users in different sessions is obtained, as shown in Table 2, where the first row represents a session of user u1, and the commodities operated in this session are a, b, a, a, and c. In the subsequent part, the sequence of operations of commodities in this session is directly represented by the notation of session, i.e., S1 = {a, b, a, a, c}.Table 2
Session example.
SessionUserCommodity sequenceS1u1a, b, a, a, cS2u2b, c, c, a, dS3u3m, n, n, c, nS4u4a, d, d, c, f, e


## 3.2. Problem Definition and Symbol Description

After sketching the problem scenario, the problem can be summarized as follows: mining the potential relationships between products through the user’s behavior logs, projecting the products into the feature space, and effectively clustering or classifying the products using their feature representations. In the subsequent model construction, one assumption is followed: most of the products operated by users in a session belong to one type [18]. This assumption is also illustrated and verified in the subsequent experimental section.


## 4. Model Building

A parallel CNN-BiLSTM neural network model is constructed by fusing CNN and BiLSTM. Since AIS data are time series, the BiLSTM can extract its time-series features well and better capture the correlations between historical data points. At the same time, considering that the output of each unit has different degrees of influence on the final task result, an attention mechanism is added to optimize the network and set weights for the output of each unit to make the feature extraction in this branch more reliable. CNN can extract local features well and explore deeper feature relationships in semantic features in AIS data.


### 4.1. Convolutional Neural Networks

Since AIS sample data are time series containing semantic information, it is very effective to select one-dimensional convolution for feature extraction of fixed-length AIS sample data [19]. Each sample data is denoted as S=Lati,Loni,Vlati,Vloni, i is the ith point in the sample, and the four dimensions are latitude, longitude, velocity in the latitude direction, and velocity in the longitude direction, respectively. Each sample data length is T, and feature dimension is N. The input sample is converted into an N × T-dimensional matrix, and the features are extracted from the sample data to simple patterns to higher levels by convolutional layers, the extracted features are filtered by maximum pooling layers, and overfitting is prevented by dropout layers. Finally, the CNN branching features are obtained, and the principle of CNN extraction of high-level features is shown in Figure 1; different blocks represent different data points, blue represents key data, and light blue represents the parameter matrix obtained after operation.Figure 1
AIS data convolution operation.


### 4.2. Bidirectional Long- and Short-Term Memory Network

LSTM is based on the RNN, and through the internal gate structure, it can effectively solve the gradient disappearance problem in the training of the RNN model, which has become one of the main basic models for the research of time-series-related problems [20].Each LSTM cell is shown in Figure2 in that the input consists of xt, ht−1, and ct−1, representing the current moment input, the previous moment output, and the previous moment cell state, respectively. Outputs include ht and ct−1, which represent the current moment output and the current cell state, respectively. There are 3 gates: the forgetting gate is responsible for selective forgetting of the input information; the input gate complements the output of the forgetting gate; and the output gate consolidates all information as the output and passes it to the next unit. Because of this, each cell has access to the current and previous information and, therefore, has a significant effect on the extraction of temporal features of the time series [21].Figure 2
LSTM cell structure.The BiLSTM structure is based on the LSTM with an additional layer of LSTM structure as the reverse feature extraction, and finally, the forward and reverse cell output results are fused so that the output results of each moment take into account both previous and subsequent information, which can make the timing features more comprehensive as shown in Figure3.Figure 3
Bidirectional LSTM structure.xi is the data of each moment of the ship, and Hi is the final output of each moment, which is the fusion of the forward and reverse outputs of each cell by means of a weighted summation.


### 4.3. Attention Mechanisms

The attention mechanism was first proposed for image recognition and has since been widely used in the field of natural language processing. Since it can assign different weights to the input data, it enables more important features to have a greater impact on the final output [22]. The principle is to use the normalized exponential function to map the simplified input vector to the interval [0, 1], which is the assigned “weight,” and its structure is shown in Figure 4. Since the state of the ship at different moments has different influences on the final result, I use the attention mechanism to assign weights to the output at each moment and then sum up by weighting. Then, I use the attention mechanism to assign weights to the output of each moment and then pass it through the fully connected layer to obtain more effective features.Figure 4
Attention unit structure.xi is the output of each cell of BiLSTM; f(x) is used to transform the multidimensional matrix into one dimension; W is the corresponding matrix; software is used to map the result to the interval [0, 1]; wi is the obtained weight. The specific calculation is(1)yi=fx=W∗xi,wi=softmaxyi.


### 4.4. Multitask Learning

Neural network-based multitask learning is widely used in practice. The same network structure is used to extract features and design different loss functions for joint training to achieve noise reduction and performance improvement [23, 24].The network structure is divided into two branches: the left side is the one-dimensional convolutional layer, maximum pooling layer, and dropout layer, and the right side is the BiLSTM layer, attention mechanism layer, and fully connected layer, in that order. The ship time-series data are fed into the two branches by dimensional transformation and then fused; then, the features are obtained by the fully connected layer.Since the paper is a hybrid task of classification and regression, the model output is divided into two parts: one part is the classification of ship behavior obtained through the software layer; the other part is the predicted trajectory data for the next moment obtained through the fully connected layer. The losses obtained after passing through different loss functions are combined to form loss post-backpropagation training. The cross-entropy loss function and mean squared error (MSE) [25] are chosen for the recognition task and the prediction task, respectively, as in equations (2) and (3).(2)Lr=−∑i=1nyilogy^i,(3)Lp=−1n∑i=1nyi−y^i2,where yi is the true value and y^i is the model output value.For the recognition task, the output results are first passed through the software layer to obtain the probability, and then the loss is calculated. For the prediction task, the loss is the average of the loss of each dimension because the predicted result is 4 dimensions of information, and then the two losses are fused by equation (4) to obtain the final loss function.(4)Lfull=λr·Lr+λp·Lp,where λr and λp are the two loss weights, respectively.


## 4.1. Convolutional Neural Networks

Since AIS sample data are time series containing semantic information, it is very effective to select one-dimensional convolution for feature extraction of fixed-length AIS sample data [19]. Each sample data is denoted as S=Lati,Loni,Vlati,Vloni, i is the ith point in the sample, and the four dimensions are latitude, longitude, velocity in the latitude direction, and velocity in the longitude direction, respectively. Each sample data length is T, and feature dimension is N. The input sample is converted into an N × T-dimensional matrix, and the features are extracted from the sample data to simple patterns to higher levels by convolutional layers, the extracted features are filtered by maximum pooling layers, and overfitting is prevented by dropout layers. Finally, the CNN branching features are obtained, and the principle of CNN extraction of high-level features is shown in Figure 1; different blocks represent different data points, blue represents key data, and light blue represents the parameter matrix obtained after operation.Figure 1
AIS data convolution operation.


## 4.2. Bidirectional Long- and Short-Term Memory Network

LSTM is based on the RNN, and through the internal gate structure, it can effectively solve the gradient disappearance problem in the training of the RNN model, which has become one of the main basic models for the research of time-series-related problems [20].Each LSTM cell is shown in Figure2 in that the input consists of xt, ht−1, and ct−1, representing the current moment input, the previous moment output, and the previous moment cell state, respectively. Outputs include ht and ct−1, which represent the current moment output and the current cell state, respectively. There are 3 gates: the forgetting gate is responsible for selective forgetting of the input information; the input gate complements the output of the forgetting gate; and the output gate consolidates all information as the output and passes it to the next unit. Because of this, each cell has access to the current and previous information and, therefore, has a significant effect on the extraction of temporal features of the time series [21].Figure 2
LSTM cell structure.The BiLSTM structure is based on the LSTM with an additional layer of LSTM structure as the reverse feature extraction, and finally, the forward and reverse cell output results are fused so that the output results of each moment take into account both previous and subsequent information, which can make the timing features more comprehensive as shown in Figure3.Figure 3
Bidirectional LSTM structure.xi is the data of each moment of the ship, and Hi is the final output of each moment, which is the fusion of the forward and reverse outputs of each cell by means of a weighted summation.


## 4.3. Attention Mechanisms

The attention mechanism was first proposed for image recognition and has since been widely used in the field of natural language processing. Since it can assign different weights to the input data, it enables more important features to have a greater impact on the final output [22]. The principle is to use the normalized exponential function to map the simplified input vector to the interval [0, 1], which is the assigned “weight,” and its structure is shown in Figure 4. Since the state of the ship at different moments has different influences on the final result, I use the attention mechanism to assign weights to the output at each moment and then sum up by weighting. Then, I use the attention mechanism to assign weights to the output of each moment and then pass it through the fully connected layer to obtain more effective features.Figure 4
Attention unit structure.xi is the output of each cell of BiLSTM; f(x) is used to transform the multidimensional matrix into one dimension; W is the corresponding matrix; software is used to map the result to the interval [0, 1]; wi is the obtained weight. The specific calculation is(1)yi=fx=W∗xi,wi=softmaxyi.


## 4.4. Multitask Learning

Neural network-based multitask learning is widely used in practice. The same network structure is used to extract features and design different loss functions for joint training to achieve noise reduction and performance improvement [23, 24].The network structure is divided into two branches: the left side is the one-dimensional convolutional layer, maximum pooling layer, and dropout layer, and the right side is the BiLSTM layer, attention mechanism layer, and fully connected layer, in that order. The ship time-series data are fed into the two branches by dimensional transformation and then fused; then, the features are obtained by the fully connected layer.Since the paper is a hybrid task of classification and regression, the model output is divided into two parts: one part is the classification of ship behavior obtained through the software layer; the other part is the predicted trajectory data for the next moment obtained through the fully connected layer. The losses obtained after passing through different loss functions are combined to form loss post-backpropagation training. The cross-entropy loss function and mean squared error (MSE) [25] are chosen for the recognition task and the prediction task, respectively, as in equations (2) and (3).(2)Lr=−∑i=1nyilogy^i,(3)Lp=−1n∑i=1nyi−y^i2,where yi is the true value and y^i is the model output value.For the recognition task, the output results are first passed through the software layer to obtain the probability, and then the loss is calculated. For the prediction task, the loss is the average of the loss of each dimension because the predicted result is 4 dimensions of information, and then the two losses are fused by equation (4) to obtain the final loss function.(4)Lfull=λr·Lr+λp·Lp,where λr and λp are the two loss weights, respectively.


## 5. Experiment

### 5.1. Data Processing

This study validates the experiment using an offline dataset provided by a contest on Alibaba Group’s Tianchi platform [26, 27]. This dataset is provided with 20,000 users’ behavior data on mobile, which contains millions of product information and more than 20 million lines of user actions, and its format is shown in Table 3. From Table 3, we can see that E-commerce websites accumulate a huge amount of product and user behavior information in the process of operation, and the full utilization of this behavior information will greatly contribute to the effective classification of products. Meanwhile, Figure 5 shows the distribution of the number of occurrences of products in the E-commerce website dataset used in this study compared with the distribution of the number of occurrences of words in the English corpus [28].Table 3
Data statistics.
StatisticStatistical valueNumber of users20,000Number of goods4,758,484Number of commodity categories9557Number of user operation records23,291,027Figure 5
Distribution of the number of occurrences of products on E-commerce websites and the number of occurrences of words in the English corpus.As can be seen from Figure5, the two have strong similarities: both are linear in the double-logarithmic coordinate system; they exhibit a long-tailed distribution with many items/words appearing very few times. These similarities provide strong support for the validity of the item2vec model.Firstly, the test data are preprocessed, the products with less than 50 occurrences are filtered out, and then the session is divided. After the test and comparison, 12 h is chosen as the session division interval, i.e., if the time interval between two product operations is greater than 12 h, they are divided into two different sessions [29]. After dividing the sessions, the sessions with lengths less than 10 and more than 100 are filtered because the shorter sesion may have more randomness and the longer session is likely to be a crawler’s record. Table 4 shows some statistics after the preprocessing. It can be seen that, after dividing the session, the average length of the session is 26.1, and there are 5.7 classes on average, which indicate that the hypothesis of the item2vec model in this study is reasonable.Table 4
Correlation data after preprocessing.
StatisticStatistical valueNumber of goods39,635Number of commodity categories1835Number of sessions104,171Average length of the session26.1Average number of categories in the session5.7


### 5.2. Experiment Setup

The hardware and software environment for this study is as follows: Intel Xeon(R) E5-2630 v4@2.20 GHz CPU, 256 GB RAM, Linux Debian 64-bit operating system [30].In the experiment, in addition to implementing the proposed item2vec and w-item2vec models, the following three methods are used as comparisons to obtain the feature vectors of the items:(1)
Random assignment of product features is used to obtain the product feature vectors.(2)
Collaborative filtering is a classical approach in recommendation systems, which assumes the similarity of products selected by the same user [22]. Therefore, in this study, we use users as the features of goods according to the commodity-based collaborative filtering method.(3)
Probability matrix decomposition is also a classical method for recommended systems, which decomposes the user’s rating matrix to obtain user and product vectors (PMF). In this study [23], PMF is used to decompose the user-to-item operation matrix to obtain the feature vectors of the items. For the obtained feature vectors, a logistic regression classifier [31] is used for training and prediction, and the prediction accuracy on the test set is used as a criterion by using the prediction accuracy on the test set. Also, in order to verify the effect of feature vector dimensionality, the vector dimensionality was set to different values for each test.


### 5.3. Analysis of Results

The proposed two models and the three comparison methods are first compared in terms of the accuracy of the classification results, and then the sensitivity of the parameters of the proposed two models is analyzed and compared.To verify the accuracy of the model classification, projection vector dimensions of item2vec, w-item2vec, and PMF were first set to 100, while two metrics were used for measurement, namely,(1)
Classification accuracy: the formula isacc=#True/#All, where #True is the number of correctly classified items and #All is the number of all items(2)
F1-macro: it denotes the arithmetic mean of F1 of each category, i.e., F1−macro=∑MFi/M, where Fi denotes F1 of the ith category and M denotes the number of categoriesThe experimental results of several methods in terms of classification accuracy are presented in Table5. First of all, it can be seen that, among the compared methods, the random method obviously does not have any effect because it does not use any information; the w-item2vec model proposed in this study significantly outperforms the other methods in terms of accuracy and F1-macro, which indicates the reasonableness of the proposed model; item2vec also achieves good results in accuracy when the proportion of the training set is small. On the contrary, the PMF method will be able to characterize the category with fewer items when the user-item matrix is sparse and the user is used as the feature directly. On the contrary, the PMF method decomposes the user-item matrix and does not improve much in both metrics as the training set increases, indicating that this method can only characterize some items.Table 5
Comparison of the classification results of each method on the test set.
Measurement indicatorsComparison methodTraining set proportion (%)15102050AccuracyItem2vec21.331.034.637.841.6w-item2vec29.642.947.552.257.1Random2.72.92.62.93.4I-CF8.518.624.332.943.2PMF11.115.916.918.119.2F1-macroItem2vec1.02.12.84.16.7w-item2vec2.36.18.612.420.3Random0.030.040.040.040.04I-CF0.733.25.28.614.6PMF0.30.50.60.81.5To verify the effect of the projected spatial dimension on the results, the spatial dimensions were set to 25, 50, 75, 100, 125, and 150, and the classification accuracy was calculated for different cases with different scales of training sets, and the results are shown in Figure6.Figure 6
Effect of vector space dimensionality.From Figure7, we can see that the effect of feature dimension on classification accuracy is related to the proportion of the training set used, and there are different optimal feature space dimensions for item2vec and w-item2vec with different proportions of the training set. At the same time, the effect of different feature dimensions on the classification results is not significant, which indicates that the spatial dimension of 25 can already characterize the commodity features.Figure 7
Effect of different proportions of the training set.


## 5.1. Data Processing

This study validates the experiment using an offline dataset provided by a contest on Alibaba Group’s Tianchi platform [26, 27]. This dataset is provided with 20,000 users’ behavior data on mobile, which contains millions of product information and more than 20 million lines of user actions, and its format is shown in Table 3. From Table 3, we can see that E-commerce websites accumulate a huge amount of product and user behavior information in the process of operation, and the full utilization of this behavior information will greatly contribute to the effective classification of products. Meanwhile, Figure 5 shows the distribution of the number of occurrences of products in the E-commerce website dataset used in this study compared with the distribution of the number of occurrences of words in the English corpus [28].Table 3
Data statistics.
StatisticStatistical valueNumber of users20,000Number of goods4,758,484Number of commodity categories9557Number of user operation records23,291,027Figure 5
Distribution of the number of occurrences of products on E-commerce websites and the number of occurrences of words in the English corpus.As can be seen from Figure5, the two have strong similarities: both are linear in the double-logarithmic coordinate system; they exhibit a long-tailed distribution with many items/words appearing very few times. These similarities provide strong support for the validity of the item2vec model.Firstly, the test data are preprocessed, the products with less than 50 occurrences are filtered out, and then the session is divided. After the test and comparison, 12 h is chosen as the session division interval, i.e., if the time interval between two product operations is greater than 12 h, they are divided into two different sessions [29]. After dividing the sessions, the sessions with lengths less than 10 and more than 100 are filtered because the shorter sesion may have more randomness and the longer session is likely to be a crawler’s record. Table 4 shows some statistics after the preprocessing. It can be seen that, after dividing the session, the average length of the session is 26.1, and there are 5.7 classes on average, which indicate that the hypothesis of the item2vec model in this study is reasonable.Table 4
Correlation data after preprocessing.
StatisticStatistical valueNumber of goods39,635Number of commodity categories1835Number of sessions104,171Average length of the session26.1Average number of categories in the session5.7


## 5.2. Experiment Setup

The hardware and software environment for this study is as follows: Intel Xeon(R) E5-2630 v4@2.20 GHz CPU, 256 GB RAM, Linux Debian 64-bit operating system [30].In the experiment, in addition to implementing the proposed item2vec and w-item2vec models, the following three methods are used as comparisons to obtain the feature vectors of the items:(1)
Random assignment of product features is used to obtain the product feature vectors.(2)
Collaborative filtering is a classical approach in recommendation systems, which assumes the similarity of products selected by the same user [22]. Therefore, in this study, we use users as the features of goods according to the commodity-based collaborative filtering method.(3)
Probability matrix decomposition is also a classical method for recommended systems, which decomposes the user’s rating matrix to obtain user and product vectors (PMF). In this study [23], PMF is used to decompose the user-to-item operation matrix to obtain the feature vectors of the items. For the obtained feature vectors, a logistic regression classifier [31] is used for training and prediction, and the prediction accuracy on the test set is used as a criterion by using the prediction accuracy on the test set. Also, in order to verify the effect of feature vector dimensionality, the vector dimensionality was set to different values for each test.


## 5.3. Analysis of Results

The proposed two models and the three comparison methods are first compared in terms of the accuracy of the classification results, and then the sensitivity of the parameters of the proposed two models is analyzed and compared.To verify the accuracy of the model classification, projection vector dimensions of item2vec, w-item2vec, and PMF were first set to 100, while two metrics were used for measurement, namely,(1)
Classification accuracy: the formula isacc=#True/#All, where #True is the number of correctly classified items and #All is the number of all items(2)
F1-macro: it denotes the arithmetic mean of F1 of each category, i.e., F1−macro=∑MFi/M, where Fi denotes F1 of the ith category and M denotes the number of categoriesThe experimental results of several methods in terms of classification accuracy are presented in Table5. First of all, it can be seen that, among the compared methods, the random method obviously does not have any effect because it does not use any information; the w-item2vec model proposed in this study significantly outperforms the other methods in terms of accuracy and F1-macro, which indicates the reasonableness of the proposed model; item2vec also achieves good results in accuracy when the proportion of the training set is small. On the contrary, the PMF method will be able to characterize the category with fewer items when the user-item matrix is sparse and the user is used as the feature directly. On the contrary, the PMF method decomposes the user-item matrix and does not improve much in both metrics as the training set increases, indicating that this method can only characterize some items.Table 5
Comparison of the classification results of each method on the test set.
Measurement indicatorsComparison methodTraining set proportion (%)15102050AccuracyItem2vec21.331.034.637.841.6w-item2vec29.642.947.552.257.1Random2.72.92.62.93.4I-CF8.518.624.332.943.2PMF11.115.916.918.119.2F1-macroItem2vec1.02.12.84.16.7w-item2vec2.36.18.612.420.3Random0.030.040.040.040.04I-CF0.733.25.28.614.6PMF0.30.50.60.81.5To verify the effect of the projected spatial dimension on the results, the spatial dimensions were set to 25, 50, 75, 100, 125, and 150, and the classification accuracy was calculated for different cases with different scales of training sets, and the results are shown in Figure6.Figure 6
Effect of vector space dimensionality.From Figure7, we can see that the effect of feature dimension on classification accuracy is related to the proportion of the training set used, and there are different optimal feature space dimensions for item2vec and w-item2vec with different proportions of the training set. At the same time, the effect of different feature dimensions on the classification results is not significant, which indicates that the spatial dimension of 25 can already characterize the commodity features.Figure 7
Effect of different proportions of the training set.


## 6. Conclusions

Based on the co-occurrence relationship of commodities in user operation sequences, the proposed item2vec model represents commodities as vectors in the feature space. Item2vec represents commodities that are similar in nature and category as vectors that are close to each other in the feature space, and commodities can be well classified using the embedded feature vectors of commodities. The proposed w-item2vec can better model the commodity features by considering the weight information and the influence of the number of occurrences between different commodities in the user’s commodity sequence. Experiments are conducted on a real dataset provided by the Tmall website, and the experiments demonstrate the effectiveness of the proposed model.In the future work, we will study how to integrate more different types of information into the model, for example, (1) to differentiate the user’s operation behaviors such as clicking and favoritism; (2) to model different behavioral patterns of users at different time periods; and (3) to model these different behavioral patterns separately.



---

*Source: 1725544-2021-12-15.xml*