TWI779887B - Dynamic homestay information recommendation device - Google Patents

Abstract

A dynamic homestay information recommendation device is composed of a homestay characteristic learning module, a user preference module and a recommendation module. When in use, the present invention only uses the user's browsing, clicking, and ordering records and homestay information for modeling, uses a denoising self-encoder to perform feature extraction to extract excellent homestay feature vectors, and introduces the concept of time with the user's browsing, clicking, and ordering records , through the two-way recurrent neural network combined with the attention mechanism to predict the user's preferences and recommend products to generate a recommended list of homestays. Experiments show that the device proposed by the present invention has excellent performance no matter it provides users with complete or partial browsing, clicking and ordering records. Top@5 is increased to 56.8% and MAP@5 is increased to 0.419. Experimental results verify that the device proposed by the present invention can recommend products even for new users without providing personal information and historical order records at all, and provide users with excellent homestay recommendation lists. Users gradually pay more attention to personal information era, it is evident that this device exhibits excellent potential.

Description

Dynamic homestay information recommendation device

The present invention relates to a dynamic homestay information recommendation device, especially relates to a It is suitable for room reservation recommendation systems and e-commerce recommendation system applications without the need for users to log in and user information, especially refers to products that provide fast and accurate model predictions of user preferences, helping to provide a better user experience By.

With the advancement of technology, the consumption mode has changed from bricks and mortar to online platforms. Cited in the platform Incorporating a personalized recommendation system brings huge economic benefits to e-commerce, and quickly matches products to users on highly competitive shopping platforms, thereby improving user brand loyalty to prevent user loss.

In the traditional e-commerce sales model, most of them provide search engines or filters for users to choose products, or manually group and analyze users, and deliver advertisements or coupons to specific users. However, in the face of a large number of users and transaction records, this move will consume huge company resources and a lot of cost loss. With the advent of the era of big data, data mining (Data Mining) and deep learning (deep learning) technologies can be used to quickly and automatically analyze user behavior and product information, and create an excellent personalized recommendation system.

When building a recommendation system, data collection can be divided into four types, including Commodity data, user browsing data, user transaction data, and user rating data. Generally speaking, an excellent recommendation system is built on a large amount of data sets on the e-commerce platform, but a large amount of product data and user browsing records often lead to the problem of sparse matrix, which makes data mining and traditional machine learning difficult. For example, the traditional classic personalized recommendation system such as association rule (Association rule), is based on a The product information purchased by the user within a certain period of time is used to find out the correlation between the products. However, this association rule faces the problem that the calculation cost will increase significantly when the data is huge, and it is easy to find too many or impractical product associations. In addition, another classic personalized recommendation system is collaborative filtering (Collaborative filtering), It mainly uses user transaction data and user rating data to model, forms these data into a rating matrix (rating matrix), and calculates the similarity between users or the degree of correlation between products, and then groups users and products. Actions to find user groups similar to a specific user, and finally use similar user groups to analyze the preferences of a specific user and generate products suitable for the user for recommendation. However, the collaborative filtering also faces problems such as cold start problem (that is, new user problem), sparse matrix problem, and public bias.

As can be seen from the above, the traditional method is because by analyzing and classifying users, and analyzing each Recommendations are made based on the lists purchased by category users, so when new products or new users appear, there may be cases where recommendations cannot be made. In addition, previous methods required users to input relevant information before making recommendations. In other words, most recommendation systems now rely on users’ personal information to generate personalized recommendations. As users pay more and more attention to personal information, users will face Reluctance to provide relevant information leads to serious inaccurate recommendations.

In view of the fact that the common recommendation system in the past only classified users through their behavior, or through Analyze the basic information of the product itself as a product representation, and most of them require users to log in to use their past records as the basis for recommendation. Therefore, under the rising awareness of personal data protection, it is necessary to improve the existing deficiencies and develop an invention that can solve the technical shortcomings of the previous case and can make effective recommendations without using personal data.

The main purpose of the present invention is to overcome the above-mentioned problems encountered in the prior art and provide Provides a device that can make personalized recommendations based on user preferences without user information. After testing, the recommendation accuracy can reach a high accuracy of Top@20% 75%, which can further optimize the general recommendation algorithm on the market. The accuracy of the old technology is greatly improved, which helps to provide a dynamic homestay information recommendation device that provides a better user experience.

In order to achieve the above purpose, the present invention is a dynamic homestay information recommendation device, which is used in The user does not need to log in and does not need user information, and is suitable for room reservation recommendation system and e-commerce recommendation system applications. The dynamic homestay information recommendation device includes: a homestay characteristic learning module, based on the homestay information of several homestays, using A denoising autoencoder (Denoising Autoencoder) performs dimension reduction and feature vector extraction calculations on these homestay information, and generates several lower-dimensional homestay feature vectors through the pre-training of the denoising autoencoder , and then introduce the concept of time according to the user's current browsing click (click records) selection records, sequentially extract the information of the homestays corresponding to the browsed and selected reservations from the several homestays, and encode them through the denoising self-encoder. Obtain several corresponding B&B's embedding vectors from these lower-dimensional B&B feature vectors, and at the same time output them to the Bidirectional Recurrent Neural Network (Bidirectional Recurrent Neural Network) based on the Attention Mechanism (Attention Mechanism), In the prediction model of BiRNN), on the one hand, the denoising self-encoder is used for decoding and restoration; a user preference module is connected to the homestay feature learning module, which is based on the bidirectional recurrent neural network combined with the attention mechanism. A model training is used to establish the prediction model and obtain the input feature vectors of these homestays. The feature vectors of these homestays are sequentially input into the prediction model according to the time on the user's current browsing click selection record, and the prediction model is used to learn and analyze User preferences to generate several user preference vectors, and then use the joint training method to use the denoising autoencoder and the prediction model to compare the feature vectors of these homestays with those generated by these user preference vectors error to minimize the joint error; and a recommendation module, connected to the user preference module, after the joint training, the prediction model can finally predict the most likely user from the feature vectors of these homestays Select several of the most representative homestay feature vectors, use the minimized cosine similarity between vectors to calculate the degree of correlation between each homestay, and calculate the cosine between these most representative homestay feature vectors and all homestay feature vectors According to the cosine similarity from the most representative homestay feature vectors, sort them from high to low, and extract the homestays corresponding to the N most similar homestay feature vectors in the top ranking according to the sorting, and pass The recommendation process (Recommendation process) generates a recommended list of homestays suitable for the user for recommendation; among them, the homestay feature learning module, the user preference module and the recommendation module are jointly trained to correct each other, thereby effectively improving Its recommended accuracy.

In the above-mentioned embodiments of the present invention, the homestay information includes the area, room type, and facilities of the homestay. Facilities, equipment, services, prices and other information related to homestays.

In the above-mentioned embodiment of the present invention, the browsing click selection record includes the user's current point The number of clicks, order, browsing information and the homestays selected by the user.

In the above embodiments of the present invention, the denoising self-encoder is composed of an encoder, The decoder (decoder) and the hidden layer included between the encoder and the decoder, the denoising self-encoder encodes these lower-dimensional feature vectors through the encoder, and projects them to the hidden layer layer, and then decode and restore the hidden layer through the decoder.

In the above embodiments of the present invention, the denoising self-encoder performs dimension reduction and feature orientation Before volume extraction, the system first performs one-hot encoding on the categorical features in the several homestay information, and uses encoding to amplify the dimension of the several homestay information.

In the above-mentioned embodiments of the present invention, the prediction model uses the attention mechanism to assign relations Note the weights to make predictions to output the most representative feature vectors of the most representative homestays that the user is most likely to purchase.

In the above-mentioned embodiments of the present invention, the predictive model includes One or more gated recurrent units (GRUs) output by each channel of the network.

Please refer to "Figures 1 to 15", which are respectively the dynamic homestays of the present invention Schematic diagram of the structure of the information recommendation device, a schematic diagram of the training process of the dynamic homestay information recommendation device of the present invention, a schematic diagram of the architecture of the denoising self-encoder of the present invention, a schematic diagram of the architecture of the prediction model of the present invention, a schematic diagram of the recommendation process of the present invention, and Top of the present invention @N schematic diagram, MAP@N schematic diagram of the present invention, the histogram of different models of the present invention in Top@N, the histogram of different models of the present invention in MAP@N, the dynamic recommendation schematic diagram of the present invention, and the histogram of the present invention The dynamic recommendation performance diagram of the proposed device model, the dynamic recommendation performance diagram of the AE-RNN model compared with the present invention, the dynamic recommendation performance diagram of the Wide&Deep model compared with the present invention, the dynamic recommendation performance diagram of the modified Collaborative Filtering model compared with the present invention, and the dynamic recommendation performance diagram of the present invention The length statistics graph of the sequence clicked by the user. As shown in the figure: the present invention is a dynamic homestay information recommendation device, which is composed of a homestay characteristic learning module 1, a user preference module 2 and a recommendation module 3.

The B&B feature learning module 1 mentioned above is based on the B&B information of several B&Bs. Use a denoising autoencoder (Denoising Autoencoder) 11 to perform dimension reduction and feature vector extraction calculations on these homestay information, and generate several lower dimensions through the pre-training of the denoising autoencoder 11 B&B feature vector, and then introduce the concept of time according to the user's current browsing click (click records) selection record, and sequentially extract the B&B information corresponding to the browsed and selected B&Bs from the several B&Bs, and pass through the denoising self-encoder 11 to encode, and obtain several corresponding B&B's embedding vectors from these lower-dimensional B&B feature vectors, and at the same time output them to the Attention Mechanism (Attention Mechanism) 212 bidirectional recurrent neural network ( In the prediction model 21 of the Bidirectional Recurrent Neural Network, BiRNN) 211, on the one hand, the denoising self-encoder 11 will then be used for decoding and restoration.

The user preference module 2 is connected to the homestay feature learning module 1, and the two-way transmission The neural network 211 combines the attention mechanism 212 for model training to establish the predictive model 21. Obtain the input feature vectors of these homestays. The feature vectors of these homestays are sequentially input into the prediction model 21 according to the time on the user's current browsing, clicking and ordering records, and use the prediction model 21 to learn and analyze user preferences to generate Several user preference vectors, and then use joint training (joint training), use the denoising autoencoder 11 and the prediction model 21 to compare the errors generated by these homestay feature vectors and these user preference vectors for joint training. Trained to minimize the joint error.

The recommendation module 3 is connected with the user preference module 2, and after the joint training, the The prediction model 21 can finally predict several representative homestay feature vectors that the user is most likely to purchase from these homestay feature vectors, and calculate the degree of correlation between homestays by minimizing the cosine similarity between vectors. By calculating the cosine similarity between the most representative homestay feature vectors and all homestay feature vectors, and sorting from the most representative homestay feature vectors according to the cosine similarity from high to low, according to the sorting Extract the homestays corresponding to the N most similar homestay feature vectors in the top ranking, and generate a homestay recommendation list 31 suitable for the user through the recommendation process (Recommendation process); among them, the homestay feature learning module 1, the The user preference module 2 and the recommendation module 3 are jointly trained to correct each other, thereby effectively improving the recommendation accuracy. If so, a brand-new dynamic homestay information recommendation device 100 is formed by the structure disclosed above.

The above homestay information includes the area, room type, facility, equipment, service and price of the homestay and other information related to homestays; and the browsing click selection record includes the user's current click times, sequence, browsing information and the homestays selected by the user.

The above prediction model 21 includes each One or more gated recurrent units (GRU) 213 output by each channel.

The above prediction model uses the attention mechanism 212 to assign attention weights to Prediction is performed to output several most representative homestay feature vectors that the user is most likely to purchase.

The training process of the overall model of the present invention is shown in Figure 2. The left part of the figure is a The complete denoising self-encoder 11 uses the lower-dimensional homestay feature vector generated by the denoising self-encoder 11 as input, and sequentially inputs the right part based on the attention mechanism 212 according to the user's current browsing, clicking and ordering records. In the prediction model 21 of the bidirectional recurrent neural network 211, the errors generated by both sides are combined for joint training during the training process.

為對類別特徵進行one-hot編碼後，再經過極小化極大演算法（min-Max）標準化的民宿資訊，

為加入隨即雜訊後的民宿資訊，

為民宿特徵向量，及

為去噪自編碼器的輸出亦即

的重建。該去噪自編碼器１１的重建誤差係使用均方誤差（Mean squared error, MSE）。之後將依照用戶當下之瀏覽點擊選訂紀錄依序抽取出相對應民宿資訊的隱藏層之民宿特徵向量進行後續模型的建立。 When used, the denoising self-encoder 11 performs one-hot encoding on the category features (Categorical features) in the several homestay information before performing dimension reduction and feature vector extraction. Expand the dimensions of the several homestay information. Then, use the denoising self-encoder 11 to pre-train several homestay information of the enlarged dimension to obtain a relatively low-dimensional hotel feature vector. The denoising self-encoder 11 needs to be jointly trained with the prediction model on the right. to complete the training. The architecture of the denoising self-encoder 11 is mainly composed of an encoder (encoder) 111, a decoder (decoder) 112, and a hidden layer 113 included between the encoder 111 and the decoder 112, and its architecture is as shown in Section 3. As shown in the figure. in the picture

In order to perform one-hot encoding on the category features, and then standardize the homestay information through the minimization and maximization algorithm (min-Max),

In order to add the homestay information after the immediate noise,

is the feature vector of the homestay, and

is the output of the denoising autoencoder, namely

reconstruction. The reconstruction error of the denoising autoencoder 11 uses mean squared error (Mean squared error, MSE). Afterwards, according to the user's current browsing, clicking, and ordering records, the hotel feature vectors of the hidden layer corresponding to the hotel information will be sequentially extracted for the establishment of subsequent models.

The present invention extracts from several homestays based on the user’s browsing, clicking, and ordering records for homestays. Take out the homestay information corresponding to the browsing and ordering, and encode it through the encoder 111 in the denoising self-encoder 11, obtain the corresponding homestay feature vector, and input it into the bidirectional recurrent neural network 211 for follow-up training.

為基於注意力機制雙向遞迴神經網路的輸出，

與

的維度相同，

為訂購的民宿特徵向量，

為非瀏覽或訂購過的民宿特徵向量，

為經過one-hot再經過min-Max標準化的民宿資訊，

則為去噪自編碼器的輸出即民宿資訊的重建。 Figure 4 shows the details of the prediction model of the two-way recurrent neural network constructed based on the attention mechanism on the right side of Figure 2.

is the output of the bidirectional recurrent neural network based on the attention mechanism,

and

have the same dimensions,

is the feature vector of the ordered homestay,

is the feature vector of non-browsed or ordered homestays,

For homestay information standardized by one-hot and then min-Max,

It is the output of the denoising self-encoder, that is, the reconstruction of homestay information.

：

It can be seen that the present invention uses the encoder 111 of the denoising self-encoder to encode, and then inputs it to the decoder 112 of the denoising self-encoder and the bidirectional recurrent neural network 211 based on the attention mechanism 212, and minimizes the combined error

:

由

與

兩部分所組成，由此可看出去噪自編碼器所訓練出的民宿特徵向量同時參與資訊重建以及預測的任務。 It can be observed from the formula that

Depend on

and

It is composed of two parts. It can be seen that the feature vector of the homestay trained by the denoising self-encoder participates in the tasks of information reconstruction and prediction at the same time.

，接著將利用

來計算與所有民宿特徵向量的餘弦相似度，餘弦相似度越高越能代表該民宿與用戶偏好的民宿越相似，因此可合理的將與用戶偏好相似的民宿進行餘弦相似度排序，依照相似度高低為用戶生成出適合的民宿推薦清單，其簡易推薦流程如第５圖所示。 In the final test/recommendation stage, the present invention will use the bidirectional recurrent neural network based on the attention mechanism to predict the feature vector of the homestay that the user may eventually order

, then use the

To calculate the cosine similarity with all homestay feature vectors, the higher the cosine similarity, the more similar the homestay is to the homestay preferred by the user. Therefore, it is reasonable to sort the cosine similarity of the homestay similar to the user's preference, according to the similarity High and Low generates a suitable homestay recommendation list for users, and its simple recommendation process is shown in Figure 5.

，而圖中的黑色圓點則為預測模型的輸出即最終可能訂購民宿的民宿特徵向量

，黑色圓圈則為經過餘弦相似度計算過後所篩選出的候選民宿特徵向量，最終本發明將選取前N個高餘弦相似度的民宿作為民宿推薦清單３１。 The lower left corner of Figure 5 is a two-dimensional visualization diagram of the t-distributed Stochastic Neighbor Embedding (t-SNE) method of the feature vector of the homestay after joint training. Each point in the figure represents a homestay. The user's browsing, clicking, and ordering records are input into the device 100 to generate

, and the black dots in the figure are the output of the prediction model, that is, the feature vector of the homestay that may eventually order a homestay

, the black circles are the feature vectors of candidate homestays screened out after cosine similarity calculation, and finally the present invention selects the top N homestays with high cosine similarity as homestay recommendation list 31.

The following examples are only examples for understanding the details and connotation of the present invention, but not for limitation The patent scope of the present invention.

[Evaluation Criteria] In this experiment, after the recommendation list is generated, the model effect will be evaluated with Top@N and Mean average precision@N, MAP@N. The definition of Top@N is assuming that when the user browses, clicks on the selected record and enters the N homestay recommendation list generated by the device 100, if the user’s final ordered homestay appears, it means that the recommendation is successful, and the recommendation ratio of Top@N is recommended The proportion of successful users relative to the overall recommended users (all users in the test set) is shown in Figure 6.

It can be seen from Figure 6 that the recommendation list generated by the first browsing click and ordering record is more successful than the recommendation list generated by the second browsing click and ordering record, because it is hoped that the order in the recommendation list Homestays appear first, so the present invention also uses MAP@N to evaluate the model effect. The definition of MAP@N is to assume that when the user browses, clicks on the selected record and enters the N homestay recommendation list generated by the device 100, the user appears The final ordering homestay takes into account the location of the homestay, and the average accuracy (average precision, AP) is calculated by the reciprocal of the location, and the calculation of the overall MAP@N is the average AP of each record, as shown in Figure 7 Show.

Observing Figure 7, it can be found that when the first homestay in the generated recommendation list is the homestay that the user finally ordered, it means that the AP@5=1 of this data, and when the second homestay in the recommendation list is the final homestay that the user ordered For homestay, it means AP@5=0.5 of this data. In this way, it can be deduced that when all the data are evaluated and the MAP@5 is between 1 and 0.5, it shows that the device performs very well, and the generated recommendation lists are all between the first and second homestays.

[Comparison with different recommendation methods]

This device will work with random recommendation (Random), Distance-Price recommendation, Adaptive Embedding-based Recurrent Neural Network (AE-RNN) model, modified collaborative filtering (modified Collaborative Filtering) And the existing recommendation system model Wide & Deep model compares Top@N and MAP@N, and explores how much the recommendation effect can be improved by using different recommendation methods for the existing structure of A company, that is, the evaluation benchmark (AY sort), and further analyzes Compare the differences of different recommended methods.

The following table 1 shows the recommendation effects of different recommendation models. The values in the table are 2004 user clicks and browsing records in January 2019. Top@N and MAP@N are used for evaluation and testing respectively.

Table I Top@20 Top@10 Top@5 MAP@20 MAP@10 MAP@5 This device 0.745 0.662 0.568 0.434 0.432 0.419 AE-RNN 0.532 0.453 0.375 0.286 0.281 0.270 Wide&Deep 0.490 0.391 0.309 0.225 0.218 0.207 modified CF 0.458 0.361 0.263 0.165 0.158 0.145 Distance-Price 0.306 0.198 0.129 0.081 0.074 0.064 AYsort 0.312 0.189 0.120 0.079 0.070 0.062 Random 0.089 0.047 0.024 0.017 0.014 0.011

The random recommendation is based on the B&Bs that the user currently clicks and browses, according to a fixed distance The range randomly recommends N homestays in the city to the user. The Distance-Price recommendation uses the characteristics of homestays related to the amount and location to filter out homestays to generate a recommendation list. Modified Collaborative Filtering only uses the homestay records that the user has clicked to find similar user click records. Homestay recommendation, the current recommendation system Wide&Deep model uses the feature vector of the homestay clicked by the user to make predictions through the model, and the AE-RNN model only uses the denoising self-encoder for feature extraction and then passes through the one-way recurrent neural network to generate recommendations list is recommended.

From the results in Table 1 above, it can be seen that the device, AE-RNN model, Wide&Deep The recommendation effect of the model and modified Collaborative Filtering is better than that of AY sort in Top@N or MAP@N and much higher than that of random recommendation, while the recommendation effect of Distance-Price recommendation is similar to that of AY sort. A bar chart comparing each model is shown in Figures 8 and 9.

From Figure 8 and Figure 9, it can be found that through modified Collaborative Filtering, The comparison between Distance-Price recommendation and random recommendation shows that the user's complete browsing and clicking records have a considerable impact on the model recommendation. Modified Collaborative Filtering understands user preferences through the user's clicking and browsing records, and then recommends the homestays that users with similar clicking and browsing records finally ordered. to that user. Homestay features longitude, latitude, and min_price are relatively important homestay features among all homestay features. Distance-Price recommends that the user's preference for homestay price and location be mapped out through the user's complete click browsing record. This approach achieves a recommendation effect similar to AY sort.

Through the comparison of modified Collaborative Filtering and Wide&Deep model, it can be found that Now, only using the user's click record and matching the corresponding homestay information can help the Wide&Deep model learn the combination of high-level and low-level homestay features, so that the model can better understand the user's preferences and generate an excellent recommendation list for recommendation.

And by comparing the Wide&Deep model with the AE-RNN model, it can be known that the user's click Browsing records plus homestay information, coupled with the introduction of time series information can help the model to improve the recommendation effect again. Training an excellent homestay information vector can not only help the model better understand user preferences, but also use the homestay room when recommending homestay to users. cosine similarity to generate a recommendation list.

Finally, comparing the AE-RNN model with this device, it can be seen that it can provide more homestay information It helps to improve the difference between homestays and generate better recommendation lists, and the number of clicks by users on homestays also helps the model understand the user's attention to different homestays, and conduct joint training through a two-way recurrent neural network and attention mechanism It can effectively help the recommendation results of the overall model.

[Dynamic recommendation] From the results in Table 1, it can be seen that the comparison of recommendation effects obtained by inputting the user's complete click record into the model, but in the real network platform interface, it is impossible to obtain the user's complete click browsing record from the beginning until the user completes the order . In actual situations, the present invention must dynamically provide a recommendation list according to the user's current click situation, and expects to provide an excellent recommendation list in a limited number of clicks to browse homestays.

In view of the fact that no AB test (A/B test) has been given on the platform, the present invention tries to simulate It is proposed that the user click to browse the homestay on the platform of Company A, according to the current click sequence on the test set, the dynamic Provided to the device to generate recommendation lists sequentially, and evaluate the performance of Top@N and MAP@N according to each generated recommendation list. However, it needs to be emphasized that since the evaluation of dynamic recommendations cannot be performed on the platform, only the static webpage fixed sorting mechanism can be used to provide the device with the orderly selection records of each current user's browsing and clicking to generate a recommendation list. That is to say, the user still sees the original recommendation list, and each click has not been affected by the recommendation list generated by the model. However, the present invention expects that the dynamic recommendation in reality will provide better performance because it can actually dynamically adjust the recommendation list, so that the model can more quickly predict the user's final order for a homestay.

接著本發明將依照點擊的順序依序將瀏覽點擊選訂紀錄輸入至本裝置１００當中去生成民宿推薦清單３１，如第１０圖所示。 Assuming that the user browses, clicks and selects the order record in the following form

Then the present invention will sequentially input the browsing, clicking and ordering records into the device 100 according to the order of the clicking to generate the hotel recommendation list 31, as shown in FIG. 10 .

Among the six models in Table 1, the models using user click sequences for modeling are respectively The proposed device model, AE-RNN model, Wide&Deep model and modified Collaborative Filtering will be followed by dynamic recommendation evaluation for these four models. The test results are shown in Figures 11, 12, 13 and 14.

From the performance of each model in the figure, it can be observed that the device model and the AE-RNN model And the Wide&Deep model will improve the accuracy of the homestay recommendation list as the length of the click sequence increases. The device model and the AE-RNN model present the best recommendation status when the sequence length reaches 50 clicks, and then clicks with the sequence length The number of clicks increases to maintain the accuracy of the recommendation list, while the Wide&Deep model ignores the time series information and is less able to distinguish the user's real preference under multiple click sequences, so the performance is not as good as the device model and the AE-RNN model excellent.

It can be seen that the device model, AE-RNN model and Wide&Deep model are less susceptible to The accuracy of the recommended list is reduced due to the impact of redundant clicks, and it has the ability to resist noise. You can also see The device model not only has better performance than the AE-RNN model, but also has better stability.

In contrast, the dynamic recommendation performance diagram of modified Collaborative Filtering (see Figure 14 As shown), it can be found that Top@N maintains a steady growth with the increase of the length of the click sequence, but in the performance of MAP@N, it can be found that the highest value is shown when the length of the click sequence reaches 12 clicks, and then with the sequence The number of length clicks increases and the recommendation effect decreases. It can be seen that when the length of the click sequence increases gradually, the ability of modified Collaborative Filtering to judge user preferences will gradually decrease. Although the generated recommendation list has the final ordered homestay, it will eventually order The B&Bs are placed at the lower end of the list, which is why the performance is stable in the Top@N evaluation, but the MAP@N evaluation is declining.

The four groups of models can be found after the dynamic recommendation test, the device model, AE-RNN model And the Wide&Deep model tends to enter the complete click sequence length to achieve the best recommendation status. From Figure 15, it can be found that when the sequence length accumulates to 50 clicks, it reaches 1913, accounting for 95% of the total, and the length of the click sequence is too long. It will affect the modified The recommended effect of Collaborative Filtering.

In this way, the present invention collects the information of each homestay and the complete browsing clicks of all users After selecting and ordering records, by observing these data, it is found that the behavior of users repeatedly clicking on the same homestay indicates that they have a high degree of attention to the homestay, and complete homestay information helps to increase the difference between homestays. In addition, the present invention uses the denoising self-encoder to perform feature extraction on homestay information to obtain excellent homestay feature vectors, and then uses the user's complete browsing and clicking to select records, and uses a two-way recurrent neural network with an attention mechanism. Predict the user's favorite homestay and recommend it. Finally, the present invention compares the model of the proposed device with the previously proposed model and the current recommendation system of Company A. After testing, the results show that whether it provides users with complete or partial browsing, clicking and ordering records, the device model has Excellent performance results and greatly increase the probability of successful recommendation.

In summary, the present invention is a dynamic homestay information recommendation device, which can effectively improve Various shortcomings that are commonly used, without user information, personalized recommendations can be made according to user preferences. After testing, the recommendation accuracy can reach a high accuracy of 75%, which can further optimize the general recommendation algorithm on the market and greatly improve The accuracy of the old technology helps to provide a better user experience, thereby making the present invention more advanced, more practical, and more in line with the needs of users, which indeed meets the requirements of the invention patent application. File a patent application in accordance with the law.

However, what is described above is only a preferred embodiment of the present invention, and should not be limited thereto. The scope of implementation of the present invention; therefore, all simple equivalent changes and modifications made according to the patent scope of the present invention and the content of the description of the invention should still fall within the scope covered by the patent of the present invention.

100:Dynamic homestay information recommendation device 1: B&B feature learning module 11: Denoising autoencoder 111: Encoder 112: Decoder 113: Hidden layer 2: User Preferences Module 21: Predictive Models 211: Bidirectional Recurrent Neural Networks 212: Attention mechanism 213: Gated structures 3: Recommended modules 31: Homestay recommendation list

Figure 1 is a schematic diagram of the structure of the dynamic homestay information recommendation device of the present invention. Figure 2 is a schematic diagram of the training process of the dynamic homestay information recommendation device of the present invention. Figure 3 is a schematic diagram of the architecture of the denoising self-encoder of the present invention. Figure 4 is a schematic diagram of the structure of the prediction model of the present invention. Fig. 5 is a schematic diagram of the recommended flow chart of the present invention. Figure 6 is a schematic diagram of Top@N of the present invention. Figure 7 is a schematic diagram of the MAP@N of the present invention. Figure 8 is the histogram of different models of the present invention in Top@N. Figure 9 is a histogram of different models of the present invention in MAP@N. Figure 10 is a schematic diagram of the dynamic recommendation of the present invention. Fig. 11 is a dynamic recommendation performance diagram of the device model proposed by the present invention. Fig. 12 is a dynamic recommendation performance diagram comparing the AE-RNN model of the present invention. Fig. 13 is a dynamic recommendation performance diagram of the comparison of Wide & Deep models in the present invention. Fig. 14 is a dynamic recommendation performance diagram comparing modified Collaborative Filtering models of the present invention. Fig. 15 is a statistical diagram of the length of the user click sequence of the present invention.

100:Dynamic homestay information recommendation device

1: B&B feature learning module

11: Denoising self-encoder

2: User preference module

21: Predictive Models

211: Bidirectional Recurrent Neural Networks

212:Attention mechanism

213: Gating structure

3: Recommended modules

31: Homestay recommendation list

Claims (7)

A dynamic homestay information recommendation device, which is suitable for room reservation recommendation systems and e-commerce recommendation systems without the need for users to log in and user information. The dynamic homestay information recommendation device includes: A B&B feature learning module, based on the B&B information of several B&Bs, uses a denoising autoencoder (Denoising Autoencoder) to perform dimension reduction and feature vector extraction calculations on these B&B information, through the denoising autoencoder Pre-training (pre-train) generates several low-dimensional homestay feature vectors, and then introduces the concept of time according to the user's current browsing click records (click records), and sequentially extracts the browsed and selected homestays from the number of homestays. The corresponding B&B information is encoded by the denoising self-encoder, and several corresponding B&B's embedding vectors are obtained from these lower-dimensional B&B feature vectors, and at the same time output to the attention-based In the Bidirectional Recurrent Neural Network (BiRNN) prediction model of the Attention Mechanism, on the one hand, the denoising self-encoder will then be used for decoding and restoration; A user preference module, connected to the homestay feature learning module, uses the bidirectional recurrent neural network combined with the attention mechanism as a model training to establish the prediction model, obtains the input feature vectors of the homestay, and the The B&B feature vectors are sequentially input into the prediction model according to the time of the user's current browsing, clicking, and ordering records. The prediction model is used to learn and analyze user preferences to generate several user preference vectors, and then adopt joint training (joint training) way, using the denoising self-encoder and the predictive model to compare the errors generated by the feature vectors of these homestays and the user preference vectors for joint training to minimize the joint error; and A recommendation module, connected to the user preference module, after the joint training, the prediction model can finally predict the most representative characteristics of several homestays that the user is most likely to purchase from the feature vectors of these homestays vector, using the minimized cosine similarity between the vectors to calculate the degree of correlation between each homestay, by calculating the cosine similarity between the most representative homestay feature vectors and all homestay feature vectors, and from the most representative homestay feature vectors The B&B feature vectors are sorted from high to low according to the cosine similarity, and according to the sorting, the B&Bs corresponding to the N most similar B&B feature vectors in the top ranks are extracted, and the recommendation process (Recommendation process) generates a list that is suitable for the user The homestay recommendation list is recommended; wherein, the homestay feature learning module, the user preference module and the recommendation module are jointly trained and can be calibrated with each other, thereby effectively improving the accuracy of their recommendation. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the homestay information includes the area, room type, facilities, equipment, services, prices and other information related to the homestay. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the record of browsing, clicking and ordering includes the user's current click times, order, browsing data and the homestays selected by the user. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the denoising self-encoder is composed of an encoder, a decoder, and a device between the encoder and the decoder. The denoising self-encoder encodes these lower-dimensional feature vectors through the encoder, and projects them into the hidden layer, and then decodes and restores the hidden layer through the decoder. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the denoising self-encoder first uses the category features (Categorical feature) in several homestay information before performing dimension reduction and feature vector extraction. ) to perform one-hot encoding to amplify the dimensions of the several homestay information. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the prediction model uses the attention mechanism to assign attention weights to make predictions, so as to output the most likely purchases by the user. Representative homestay feature vector. According to the dynamic homestay information recommendation device described in Item 1 of the scope of the patent application, the prediction model includes one or more gating structures (gated recurrent unit, GRU).

Images