• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Expert Knowledge-Based Apparel Recommendation Question and Answer System

    2022-03-08 13:11:34LIUXunSHIYouqun史有群LUOXinZHUGuoxue朱國(guó)學(xué)
    關(guān)鍵詞:國(guó)學(xué)

    LIU Xun(劉 栒), SHI Youqun(史有群), LUO Xin(羅 辛), ZHU Guoxue(朱國(guó)學(xué))

    1 College of Computer Science and Technology, Donghua University, Shanghai 201620, China2 China Textile Enterprise Association, Beijing 100020, China

    Abstract: Aiming at the lack of professional knowledge to guide apparel recommendation, an apparel recommendation method based on image design expert knowledge has been proposed. Then, apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A) system has been designed and implemented. The question templates in the apparel recommendation domain were defined, the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF) model, and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF) algorithm. The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers, and iFLYTEK’s voice application programming interface(API) was called to implement the Q&A. The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.

    Key words: expert knowledge; apparel recommendation; knowledge graph; question and answer(Q & A) system; speech recognition

    Introduction

    In recent years, with the rapid development of clothing e-commerce, online shopping clothing has become a popular trend. However, the massive growth in the amount of online clothing information has made it difficult for users to shop for their preferred and suitable clothing. As a result, there has been an increasing amount of interest in the study of apparel recommendations. At present, most recommendation systems use the recommendation method based on the user’s purchase history. For example, apparel recommendations by the user’s history of visits, combined with visual features of clothing images, which requires the user’s history of visits as a prerequisite. Zhang[1]proposed a big data causal recommendation method that used user’s history and Bayesian network for causal discovery, and constructed an apparel recommendation model that considered only clothing brands and hues. At present, the most widely used recommendation method based on history is collaborative filtering algorithm, but it suffered from data sparsity and cold start problems that still need to be addressed. However, users rarely buy the same clothes when purchasing clothing[2]. Therefore, the traditional apparel recommendation based on user’s history[3]does not consider the user’s feature information. The results of apparel recommendations obtained by this method are somewhat blind. This recommendation method not only fails to personalize the recommendation, but also increases the cost of wearing. Compared with the recommendation method based on history, the recommendation method based on knowledge has higher application value in the field of apparel recommendations. Liuetal.[4]established a knowledge base by collecting expert opinions and literatures on clothing, expressed the knowledge by generative rules, and realized intelligent recommendation of clothing based on forward linking reasoning techniques. Feng[5]established a clothing knowledge system database based on the apparel recommendation experience provided by experts, and developed a personalized apparel recommendation system based on expert knowledge. Some studies[6-7]only focused on a single characteristic of the user, the scope of recommendation was limited, and the recommendation results were not practical.

    Question and answer(Q&A) system[8]is an efficient form of natural language processing that can better recognize the intention of a user’s question than a search engine, leading to an answer that meets the user’s needs. Traditional Q&A systems rely on unstructured data crawled from the web and suffered from large amounts of redundant information and inefficient retrieval. The knowledge graph[9]as a semantic network, contains rich semantic information and provides a high-quality structured data source for Q&A systems, improving the accuracy and retrieval efficiency of Q&A. In recent years, Q&A systems based on knowledge graphs[10-11]have received widespread attention. For example, Duetal.[12]designed an intelligent Q&A system for Chinese knowledge graphs in the field of e-commerce, and realized knowledge Q&A in the field of e-commerce. Chenetal.[13]used knowledge graph technology to realize Q&A in the field of Chinese medicine. Current research has shown that the application of knowledge graph technology has improved the retrieval efficiency and accuracy of Q&A systems, but how to identify user’s intention and transform user’s question statements into query statements is still a problem that needs to be solved in order to build a Q&A system based on knowledge graph.

    To address the above problems, this paper proposed an apparel recommendation method based on image design expert knowledge, and used knowledge graph technology to build an apparel recommendation intelligent Q&A system. First, the image design expert rule system was designed and the apparel recommendation knowledge graph was created. Then, question statement processing was performed, and question statement entity recognition was performed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF) model, combined with term frequency-inverse document frequency(TF-IDF) text similarity algorithm to match question statement templates. Finally, cypher query statements were generated to retrieve the knowledge map for answers, combining with voice Q&A technology to enable users to quickly select the most suitable clothing for themselves.

    1 Knowledge Graph Construction for Apparel Recommendations

    The key to the expert knowledge-based personalized apparel recommendation lies in the design of clothing domain rules and the construction of knowledge base. In this paper, the information association between human body features and clothing features was achieved through the knowledge graph of apparel recommendation, and the image design expert rule system and the construction method of the knowledge graph were introduced below respectively.

    1.1 Image design expert rule system

    This paper addressed the lack of professional knowledge to guide apparel recommendations, combined with the knowledge of image design experts to provide a comprehensive quantitative analysis of three elements of aesthetics(style, color, and fabric) and concluded a set of quantitative aesthetic criteria suitable to apply online. More than 50 labels were designed for a single garment, including garment color, category, material, pattern, style, and occasion labels, as shown in Table 1. At the same time, combining expert matching experience and relevant information, the user image was quantified into a label system, which contains seven skin colors, nine face shapes, ten body types characteristics and height-to-weight ratio, as shown in Table 2.

    Table 1 Clothing label information

    Table 2 User image information

    Based on the above-mentioned labeling system, we proposed a personalized apparel recommendation method based on the knowledge of image design experts, combined with the apparel recommendation experience of image design experts, and summarized the matching rules of user characteristics and clothing characteristics elements. Firstly, we considered the matching rules of the elements of clothing characteristics themselves and used fuzzy logic to express the degree of matching of clothing, where 0-0.1 means “very poorly matched”, 0.2-0.4 means “poorly matched”, 0.6-0.8 means “matched” and 0.9-1.0 means “very well matched”. The matching rules for some of clothing categories are shown in Table 3, with jeans and shirts having a matching degree of 0.9, which means “very well matched”.

    Table 3 Examples of matching rules for clothing categories

    The apparel recommendations aim to provide users with professional dressing guidance, so it is important to consider not only the matching of clothing elements, but also the matching of clothing elements with elements of human characteristics. Body characteristics include skin color, face shape, body shape and height-to-weight ratio, which are mainly considered to match with clothing style and color rule. Face shape, body shape and height-to-weight ratio determine the match between the user and the clothing style, and skin color and height-to-weight ratio determine the match between the user and the clothing color. Specifically, the choice of collar shape has a direct impact on face shape, and the right collar shape can flatter the face. Both body type and height-to-weight ratio correspond to the overall label of the clothing style. Body type and height-to-weight ratio directly influence the choice of clothing style, and choosing the right clothing style can conceal and modify body flaws, amplify strengths. There is also a close relationship between clothing color and body weight, and the right color combination can visually correct the shape. According to the height-to-weight ratio of people, people have been divided into five types, and experts have given rules for matching these five types with clothing colors. For example, short thin people are suitable for mild colors in clothing with the same color scheme, and not for very dark colors or very light colors.

    1.2 Knowledge graph construction method

    Based on the image design expert rule system, a knowledge graph of apparel recommendation was constructed. The image design expert rules were stored in the Neo4j graph database in the form of triples of(clothing features, suit_for, body features), and the nodes in the knowledge graph were the label information of clothing elements and body feature elements. The relationship “suit_for” between clothing features and body features was defined, and expert rules were added to the relationships as weights in order to quantify and compare the relationships. The personalized apparel recommendations were achieved by using the semantic information contained in the knowledge graph. Firstly, the image expert rules were represented in comma-separated values(csv) form to construct the body feature entity body.csv, the clothing information entity clothes.csv and the relationship suit_for.csv. The classification labels corresponding to color, style, material, pattern and style were added to the clothing node as clothing feature attributes. Then classification labels corresponded to skin color, face shape, body type and height-to-weight ratio were tags as body feature attributes to the body feature node. Then, the csv files were imported via the “neo4j-admin import” command to generate the nodes and relationships. Finally, the apparel recommendation knowledge graph was successfully constructed.

    2 Q&A System Design

    Current recommendation systems have been well used in various areas, but lack efficient and intelligent human-computer interaction in the field of apparel recommendation. In order to address this shortcoming, this paper has adopted the form of voice Q&A, combined with the apparel recommendation knowledge graph, to make accurate and efficient apparel recommendation solutions for users according to expert rules. The Q&A system in this paper has been divided into a question semantic understanding module and a knowledge base answering module, and the details of the two modules are as follows.

    2.1 Question semantic understanding module

    After being obtained the question text by calling the speech recognition application programming interface(API), the function of question semantic understanding was implemented mainly by entity recognition and template matching methods.

    2.1.1Appareldomainentityrecognition

    To address the complex entity naming rules in the apparel domain and the incorrect recognition of entity boundaries by traditional entity recognition models, this paper has annotated the apparel recommendation corpus with begin-inside-outside(BIO) in a semi-automated manner and used it as input to the BERT-BiLSTM-CRF entity extraction model. The entity recognition model BERT-BiLSTM-CRF in this paper not only resolved the problem of poor recognition of ambiguous words by the word to vector(word2vec)[14]model, but also could well fuse the contextual information of words and contextual label information. First, word vectors of interrogative sentences were obtained by training the BERT model[15]. Then, the BiLSTM[16]was used for context encoding to obtain feature information for named entity recognition. Finally, the output sequence of the BiLSTM was annotated by the CRF[17]layer to obtain the global optimal sequence. The framework of the entity recognition model was shown in Fig. 1, which used a corpus of BIO annotated clothing questions and the answers as the model training dataset with five labels, namely “O”, “B-Body”, “I-Body”, “B-Clothes”, and “I-Clothes”. The model training was implemented in Python 3.7 and the training environment was built on the Tensorflow platform. The training environment was based on the Tensorflow 2.2.0, Ubuntu 16.04 operating system, and Tesla K80 GPU accelerated graphics card. To prevent overfitting problems, the dropout was used in the input and output of the BiLSTM with a value of 0.5.

    Fig. 1 Frame of BERT-BiLSTM-CRF model

    The Q&A system in this paper was based on the BERT-BiLSTM-CRF entity extraction model, which solved the problem that the traditional word vector representation was single and could not deal well with the multi-sense characteristics of words. Even for semantically complex question sentences, the apparel recommendation-related entities could be extracted from the questions, and combined with question template matching to complete question recognition.

    2.1.2Questiontemplatematching

    There are three main approaches to question and sentence parsing, including template matching-based approaches[18], semantic extraction based on syntactic analysis[19], and semantic extraction based on short text similarity[20]. The apparel recommendation domain is a proprietary domain with a limited variety and scope of questions, so this paper has used template matching for question and sentence parsing. And user’s intention matching is performed by calculating the similarity between the questions and template questions. The specific flow of template matching in this paper is as follows.

    According to the apparel recommendation rules, this paper designed six types of question templates based on user’s intention, and the information of the set of question templates were shown in Table 4. Among them, user’s intention was further divided into objective intention and subjective intention. Objective intention refers to entities, attributes and relationships that can correspond to the knowledge graph; subjective intention cannot be mapped to specific entities and does not require retrieval of answers in the database. For subjective intention, the corresponding response templates were set and output directly to the user through speech; for objective intention, the corresponding clothing entities or human characteristic entities need to be identified through entities. The question templates with the highest similarity were obtained through similarity matching with the question templates, and then the questions were transformed into cypher query statements, and the corresponding relationships or attributes were retrieved in Neo4j, and these entities and the corresponding relationships were filled into the response template to generate natural language answer sentences.

    Table 4 Problem template set corresponding to different intentions

    Based on the entities identified in the entity recognition session, text similarity matching was performed with the question templates, and the question template with the highest similarity was selected. In this paper, the TF-IDF algorithm[21]was adopted for text similarity matching.

    (1)

    whereTdenotes the TF of the word in the problem. The largerTis, the more representative it is in the problem.Adenotes the number of occurrences of a word in the question, andSdenotes the total number of words in the question.

    The IDF value was calculated by

    (2)

    whereIdenotes the IDF value. The product ofTandIrepresented the weight of the word in the current problem.Ddenotes the total number of template questions, andD′ denotes the number of template questions containing the word.

    A weighted sum of all the word vectors in the problem was performed to obtain the template problem vectorXand the user problem vectorY. The cosine between the sentence vectors was calculated by

    (3)

    whereCwas denoted to obtain the similarity between the proposed problem and the template problem.

    To ensure the accuracy of the questions and avoid meaningless searches of the knowledge base, a threshold of 0.5 has been set for similarity, and when the calculated similarity is greater than the threshold, a reply or query database operation would be performed. Otherwise, the user would be prompted to re-enter the question.

    After the entities in question have been identified and matched to the template questions, the relationship names have been obtained based on the mapping relationship between the template questions corresponding to the user’s intension and the database, and combined with the names of the identified apparel recommendation entities, cypher query statements have been generated according to the rules. The corresponding relationship between question sentences and query sentences is shown in Table 5.

    Table 5 Examples of cypher query corresponding to user’s questions

    The cypher query statement retrieves answer entities or attributes queried from the Neo4j graph database, filled in with answer sentence templates, and generated natural language answers, finally the answer sentences were output via speech.

    2.2 Knowledge base answering module

    2.2.1Recommendationmethod

    The apparel recommendation method in this paper was by mapping human body characteristic elements to clothing characteristic elements, as shown in Fig. 2.

    Fig. 2 Mapping relationship between the two types of characteristic elements

    The suitabilitySi(i=1, 2, 3, 4) in the graph is the weight of “suit_for” relationship between human body features and clothing features, andS′i(i=5, 6) indicates the suitability of the clothing itself. The user inputs the information of human body features, gets the suitability of the corresponding clothing features from the apparel recommendation knowledge map, and calculates the comprehensive suitability score of the recommended clothing according to

    S=W1S1+W2S2+W3S3+W4S4+S′5+S′6,

    (4)

    whereW1+W2+W3+W4=1,Wi(i=1, 2, 3, 4) indicates the influence score of each human body feature in apparel recommendation, which is given by experts, and the corresponding influence differs for different user features. In the apparel recommendation process, the user pattern that matches all the information is first found. Then the matching degree of clothing and user is calculated and arranged in descending order. Finally, the clothing of the top six clothing patterns are recommended for the user.

    2.2.2Answersentenceconstruction

    In this paper, a template approach was used to generate answer sentences and six corresponding answer sentence templates were designed based on six different types of user’s intention, as shown in Table 6.

    Table 6 Answer template corresponding to user’s intention

    By recognising the user’s intention and matching the appropriate reply sentence template, the reply sentences would eventually be output in natural language by the speech synthesis function of the speech technology module. For example, if the user accepted the recommended result, the corresponding “thank you” sentence would be replied to “Glad to help you, thanks for using it”. Another example is if the user types “What are the characteristics of a straight body type?” then we will identify the entity “straight body type” by the entity recognition model, determine the user’s intention to query the characteristics of the human body based on the similarity calculation, match the question template with the highest score, retrieve Neo4j with the cypher statement corresponding to the template, and get the attribute description information for “straight body type”. The attribute description information for “straight body type” is populated into the response template “The characteristics of {entity} are: {attribute}”, and the answer obtained is “The characteristics of straight body are: similar waist width, chest width, and hip width, with no curve in front”. If the user rejects this recommendation result, the next level of clothing will be recommended based on the relevance calculated by the recommendation method.

    3 Experiments and Result Analysis

    3.1 Speech recognition processing

    This paper is based on iFLYTEK’s voice API implementation. First, we register the voice dictation application on iFLYTEK’s official website to get the corresponding APIkey and APISecret. Then, the system sends a Websocket handshake request to iFLYTEK’s server through the authentication mechanism, and uploads and receives data through the WebSocket connection after a successful handshake. Finally, the returned JavaScript object notation(JSON) data are parsed as the result of speech recognition. The recognition process as shown in Fig. 3.

    Fig. 3 Speech recognition flow chart

    The formats of the audio are sampled according to the standards shown in Table 7.

    Table 7 Audio sampling standard

    According to the string parsed from the JSON returned by API, the text of the question is extracted and thus further processed.

    3.2 Analysis of expert knowledge recommendation result

    3.2.1Analysisoftheresultsoftheexpertknowledge-basedrecommendationmethod

    The dataset for this article comes from real questions related to apparel recommendations crawled from the Q&A website about Chinese clothing. We have got 672 real Q&A statements related to apparel recommendations. The recommendations are tagged based on the answers adopted by users in the website, and the content of the adopted answers is tagged as the correct recommendation result. The crawled Q&A statements were filtered for six categories of user intention, and finally 50 Q&A statements corresponding to each category of user intention were obtained, making a total of 300 apparel recommendation-related Q&A statements. The Q&A dataset was then divided into a training set and a test set in a ratio of 8∶2.

    In this paper, a apparel recommendation knowledge graph was constructed and a recommendation method based on expert knowledge was proposed. To verify the effectiveness of this paper’s recommendation method, we used precision and recall as evaluation metrics to compare it with traditional collaborative filtering algorithms. Recall rate is the ratio of the system’s recommendation results to the right recommendation results label. The greater the recall rate is, the more accurate the recommendation are. Accuracy rate is the ratio of recommendations accepted by the user to the overall set of recommendations. The higher the accuracy rate is, the better the performance of the recommendation system is.

    In the recommendation process, withMas the set of recommended results from test users,Nas the set of items actually selected by users in the test set, the accuracy ratePand recall rateRwere calculated as

    (5)

    (6)

    Five sets of experimental data were randomly selected to compare the precision of the expert knowledge-based apparel recommendation method with that of the collaborative filtering-based recommendation method as shown in Fig. 4, whereP1 andR1 denote the accuracy and recall rates of the expert knowledge-based recommendation method, respectively;P2 andR2 denote the accuracy and recall rates of the collaborative filtering algorithms, respectively.

    Fig. 4 Comparison of the results of the two recommended methods

    As shown in Fig. 4, the knowledge-based apparel recommendation method outperforms the collaborative filtering algorithm in terms of accuracy and recall rates, and has better apparel recommendation results.

    In order to further verify the practicality of this paper’s method for apparel recommendation, apparel recommendations were made for three users with different characteristics, and the matching degree of clothing with the user’s body type was calculated according to Eq.(4), as shown in Table 8.

    Table 8 Apparel recommendation results

    As an example, the recommended result for No.1 is 0.8 for light skin tone and light green color, 0.9 for oval face and round neck, and 0.6 for tops with a higher influence of face shape. Thus, it has been calculated that the matching degree of user-1’s characteristic information and light green round neck T-shirt is 0.86, which can be considered as a better match, and this result was consistent with the rules of clothing matching and in line with the aesthetics of the public.

    3.2.2Analysisoftheresultsoftheidentificationofquestionentity

    As there is no public question and answer corpus dataset related to apparel recommendation, the training and test sets are divided in a ratio of 8∶2 based on the relevant data of the questioned sentences obtained from 3.2.1 of this paper. Then, BIO annotation was performed to construct an apparel recommendation domain corpus, and the annotated corpus was used to train a BERT-BiLSTM-CRF entity recognition model.

    The entity recognition results showed that the BERT-BiLSTM-CRF model performed better in the entity recognition in the apparel recommendation domain. The effectiveness of the model has been assessed by the accuracy rateP, recall rateRand comprehensive evaluation indexF1, calculated as

    (7)

    (8)

    (9)

    whereTdenotes the number of correctly identified entities,F1denotes the number of all identified entities, andNdenotes the number of all tagged entities.

    A comparison of the three named entity recognition models, BiLSTM, BiLSTM-CRF and BERT-BiLSTM-CRF, is shown in Table 9.

    Table 9 Comparison of three models

    In processing the apparel recommendation corpus, the BERT-BiLSTM-CRF model is performed best among the three models. The Q&A system in this paper was based on the BERT-BiLSTM-CRF entity extraction model, the model solved the problem that the traditional word vector representation was single and could not handle the multi-sense features of words well. For the interrogative corpus data of apparel recommendations, the BERT model was added to the BiLSTM-CRF model for fine-tuning, and the contextual features of the text were identified by a bi-directional encoder, which effectively avoided the accumulation of errors in word separation and improved the accuracy of the subsequent entity extraction work.

    3.3 System interface display

    The system has been developed by using the Flask framework and the Q&A interface is designed as shown in Fig. 5. The left sidebar implements the navigation of the page, and the content area in the middle enables graphical representation of the quiz results, which can be entered by text or voice, followed by the voice output of the results. The content area on the right is a detailed description of the entity nodes.

    Fig. 5 Q&A interface display

    4 Conclusions

    In this paper, firstly, an intelligent apparel recommendation method based on image design expert knowledge is proposed. Then, the apparel recommendation knowledge graph is constructed as the knowledge base of the Q&A system by sorting out the expert rules. In addition, the entity recognition task of the Q&A system is done by the BERT-BiLSTM-CRF model. Finally, the system achieves personalized apparel recommendation with the help of speech technology. The results showed that the recommended clothes by the recommendation system in this paper were in line with popular aesthetics, and the human-computer interaction of voice Q&A improved the user’s retrieval efficiency.

    猜你喜歡
    國(guó)學(xué)
    國(guó)學(xué)大考堂
    “垂”改成“掉”,好不好?
    國(guó)學(xué)大考堂
    走入國(guó)學(xué)館遇見(jiàn)最美的你
    國(guó)學(xué)大考堂
    國(guó)學(xué)課
    奮斗雞—我的國(guó)學(xué)日常
    畫(huà)說(shuō)國(guó)學(xué)
    畫(huà)說(shuō)國(guó)學(xué)
    久久久色成人| 99国产精品99久久久久| 国产精品亚洲一级av第二区| 午夜福利18| 国产一区二区在线av高清观看| cao死你这个sao货| 国产亚洲欧美98| 国产激情偷乱视频一区二区| 此物有八面人人有两片| 欧美午夜高清在线| 国产成人精品久久二区二区91| 美女 人体艺术 gogo| 精品国内亚洲2022精品成人| 日本黄大片高清| 免费av毛片视频| 亚洲国产精品成人综合色| 首页视频小说图片口味搜索| 亚洲成av人片在线播放无| 免费在线观看影片大全网站| 国产黄a三级三级三级人| 男插女下体视频免费在线播放| 国产成人啪精品午夜网站| 99国产精品一区二区三区| 此物有八面人人有两片| 久久香蕉国产精品| 午夜激情欧美在线| 麻豆一二三区av精品| 国产精品乱码一区二三区的特点| 性色av乱码一区二区三区2| 床上黄色一级片| 亚洲欧美日韩卡通动漫| 99精品久久久久人妻精品| 久久久久国产一级毛片高清牌| 伦理电影免费视频| 窝窝影院91人妻| 国产在线精品亚洲第一网站| 久久热在线av| 香蕉久久夜色| 国产精品亚洲一级av第二区| 精品电影一区二区在线| 日韩欧美精品v在线| 搡老岳熟女国产| 欧美午夜高清在线| a级毛片a级免费在线| а√天堂www在线а√下载| 久久亚洲真实| 亚洲人成电影免费在线| 欧美丝袜亚洲另类 | 嫩草影院入口| 亚洲欧美日韩高清专用| 首页视频小说图片口味搜索| 日韩欧美国产在线观看| 香蕉久久夜色| 久久精品91蜜桃| 99国产极品粉嫩在线观看| 精品国产美女av久久久久小说| 国产精品自产拍在线观看55亚洲| 久久久久久久午夜电影| 又粗又爽又猛毛片免费看| 91av网一区二区| 婷婷六月久久综合丁香| 亚洲美女黄片视频| 日本五十路高清| 婷婷六月久久综合丁香| 老汉色av国产亚洲站长工具| 午夜亚洲福利在线播放| 美女cb高潮喷水在线观看 | 国产精品亚洲美女久久久| 日韩精品中文字幕看吧| 国产精品久久久久久久电影 | 99re在线观看精品视频| 97超视频在线观看视频| av在线天堂中文字幕| 嫩草影院入口| av片东京热男人的天堂| 日韩欧美在线二视频| 久久久久免费精品人妻一区二区| 亚洲成人免费电影在线观看| 在线视频色国产色| 欧美xxxx黑人xx丫x性爽| 欧美大码av| 一级黄色大片毛片| 亚洲精品国产精品久久久不卡| 亚洲人与动物交配视频| 免费在线观看视频国产中文字幕亚洲| 中文字幕人成人乱码亚洲影| 男人的好看免费观看在线视频| xxx96com| 久久久久久人人人人人| 欧美日韩乱码在线| 精品久久久久久久久久久久久| 九九在线视频观看精品| 琪琪午夜伦伦电影理论片6080| 亚洲人与动物交配视频| 日韩精品中文字幕看吧| 国产高清视频在线观看网站| aaaaa片日本免费| 色综合婷婷激情| 日韩成人在线观看一区二区三区| 国模一区二区三区四区视频 | 综合色av麻豆| 99久久综合精品五月天人人| 亚洲电影在线观看av| 黄片小视频在线播放| 精品一区二区三区视频在线 | 男人舔女人的私密视频| 国产伦人伦偷精品视频| 级片在线观看| 又粗又爽又猛毛片免费看| 偷拍熟女少妇极品色| 色综合欧美亚洲国产小说| 国产乱人伦免费视频| 欧美午夜高清在线| 国产日本99.免费观看| 欧美绝顶高潮抽搐喷水| 久久这里只有精品中国| 亚洲国产色片| 免费在线观看视频国产中文字幕亚洲| 久久久久久久久久黄片| xxxwww97欧美| 国产精品一区二区三区四区免费观看 | 国产高潮美女av| 淫秽高清视频在线观看| 日韩欧美在线二视频| 动漫黄色视频在线观看| 国产午夜精品久久久久久| 老司机午夜十八禁免费视频| 亚洲专区国产一区二区| 久久久久久久久久黄片| 国产日本99.免费观看| 又黄又爽又免费观看的视频| 18禁国产床啪视频网站| 国产成人av教育| 国产亚洲精品一区二区www| 美女午夜性视频免费| 最近最新免费中文字幕在线| 在线国产一区二区在线| 他把我摸到了高潮在线观看| 亚洲人成伊人成综合网2020| 超碰成人久久| 中文字幕人妻丝袜一区二区| 国产单亲对白刺激| 亚洲 欧美 日韩 在线 免费| 亚洲狠狠婷婷综合久久图片| 亚洲第一欧美日韩一区二区三区| 国产一区二区三区视频了| 久久久精品欧美日韩精品| 中出人妻视频一区二区| av福利片在线观看| 变态另类丝袜制服| 亚洲国产中文字幕在线视频| 男人舔女人的私密视频| 欧美成人一区二区免费高清观看 | 天天添夜夜摸| 黑人欧美特级aaaaaa片| 美女午夜性视频免费| 日本一二三区视频观看| 久久精品亚洲精品国产色婷小说| 岛国视频午夜一区免费看| 国产成人av教育| 国产毛片a区久久久久| 男女床上黄色一级片免费看| 最新在线观看一区二区三区| 精品国产超薄肉色丝袜足j| 亚洲av电影在线进入| 亚洲男人的天堂狠狠| 亚洲中文字幕一区二区三区有码在线看 | 亚洲男人的天堂狠狠| 午夜福利在线观看免费完整高清在 | 婷婷精品国产亚洲av在线| 国产成人精品无人区| 在线播放国产精品三级| 网址你懂的国产日韩在线| 亚洲美女黄片视频| 国产午夜精品久久久久久| 男插女下体视频免费在线播放| 亚洲成a人片在线一区二区| 午夜精品一区二区三区免费看| 欧美日韩精品网址| 精品久久久久久,| 久久久久久久午夜电影| 精品欧美国产一区二区三| 特级一级黄色大片| 丰满人妻熟妇乱又伦精品不卡| 看片在线看免费视频| 午夜两性在线视频| 啦啦啦免费观看视频1| 麻豆国产97在线/欧美| 男人的好看免费观看在线视频| 国产久久久一区二区三区| 国产亚洲精品av在线| 三级国产精品欧美在线观看 | 国产激情久久老熟女| 熟妇人妻久久中文字幕3abv| 99国产极品粉嫩在线观看| 成人特级av手机在线观看| 熟女人妻精品中文字幕| 国产伦精品一区二区三区四那| 亚洲精品一卡2卡三卡4卡5卡| 午夜福利欧美成人| 精品久久久久久久末码| 国产高清videossex| 国产成+人综合+亚洲专区| 亚洲欧美日韩高清专用| xxx96com| 在线观看免费视频日本深夜| 男女午夜视频在线观看| 男女之事视频高清在线观看| 成年女人毛片免费观看观看9| 一个人看视频在线观看www免费 | 国内精品一区二区在线观看| 精品国产三级普通话版| 亚洲精品中文字幕一二三四区| 在线看三级毛片| 亚洲va日本ⅴa欧美va伊人久久| 草草在线视频免费看| 嫁个100分男人电影在线观看| 观看免费一级毛片| 日本熟妇午夜| 免费看a级黄色片| 成年女人永久免费观看视频| 搡老妇女老女人老熟妇| 国产精品久久视频播放| 国产激情欧美一区二区| 成人国产一区最新在线观看| 亚洲av免费在线观看| 亚洲黑人精品在线| 欧美成狂野欧美在线观看| 日本三级黄在线观看| 国产免费男女视频| 日韩精品青青久久久久久| 琪琪午夜伦伦电影理论片6080| 可以在线观看的亚洲视频| 亚洲午夜理论影院| 97碰自拍视频| 久久久久久国产a免费观看| 久久久久国内视频| 久久久久精品国产欧美久久久| 免费av不卡在线播放| 亚洲片人在线观看| 欧美成人性av电影在线观看| 欧美极品一区二区三区四区| 国产精品亚洲美女久久久| 在线视频色国产色| 亚洲精华国产精华精| 最新美女视频免费是黄的| 三级国产精品欧美在线观看 | 99视频精品全部免费 在线 | 亚洲国产高清在线一区二区三| 叶爱在线成人免费视频播放| 日韩大尺度精品在线看网址| 国产高清激情床上av| 成人性生交大片免费视频hd| 久久精品91蜜桃| 亚洲av熟女| 看片在线看免费视频| 91麻豆精品激情在线观看国产| 国产97色在线日韩免费| 男人舔奶头视频| 禁无遮挡网站| 国产亚洲av高清不卡| 国产伦人伦偷精品视频| 国产熟女xx| 亚洲精品美女久久av网站| 午夜成年电影在线免费观看| 日本与韩国留学比较| 亚洲国产色片| 国内精品美女久久久久久| 日韩av在线大香蕉| 欧美日韩瑟瑟在线播放| 精品免费久久久久久久清纯| 亚洲一区二区三区不卡视频| 久久久国产精品麻豆| 黑人欧美特级aaaaaa片| 色吧在线观看| www.精华液| 日本五十路高清| 午夜精品久久久久久毛片777| 变态另类成人亚洲欧美熟女| 欧美激情在线99| 中文资源天堂在线| 九色成人免费人妻av| 亚洲第一电影网av| 婷婷六月久久综合丁香| 国产激情久久老熟女| 亚洲自拍偷在线| 国产真实乱freesex| 一二三四在线观看免费中文在| 亚洲九九香蕉| 小蜜桃在线观看免费完整版高清| 19禁男女啪啪无遮挡网站| 最近最新中文字幕大全电影3| 国产又色又爽无遮挡免费看| 99热6这里只有精品| 日本黄大片高清| 一边摸一边抽搐一进一小说| 欧美日韩精品网址| 在线视频色国产色| 丝袜人妻中文字幕| 国产精品亚洲一级av第二区| 亚洲精品色激情综合| 黄色片一级片一级黄色片| 国产午夜福利久久久久久| 免费人成视频x8x8入口观看| 成人国产综合亚洲| 99国产综合亚洲精品| 天堂av国产一区二区熟女人妻| 深夜精品福利| 亚洲在线观看片| 黄色女人牲交| 亚洲欧美精品综合久久99| 亚洲精品一区av在线观看| 亚洲国产色片| 久久久久久人人人人人| 在线观看免费午夜福利视频| 日韩免费av在线播放| 久久午夜亚洲精品久久| av在线蜜桃| 午夜福利视频1000在线观看| 国产亚洲精品久久久久久毛片| 国产成人精品无人区| 又黄又粗又硬又大视频| 亚洲欧美一区二区三区黑人| 免费看光身美女| 国产精品av久久久久免费| 十八禁人妻一区二区| 亚洲色图av天堂| 一夜夜www| 亚洲一区二区三区不卡视频| 观看美女的网站| 欧美高清成人免费视频www| 女生性感内裤真人,穿戴方法视频| 91老司机精品| 亚洲熟妇中文字幕五十中出| 免费观看精品视频网站| 变态另类丝袜制服| 天堂av国产一区二区熟女人妻| 一二三四社区在线视频社区8| 久久热在线av| 亚洲精品色激情综合| 欧美大码av| 欧美午夜高清在线| 国产高清三级在线| 国产高清视频在线观看网站| 精品国内亚洲2022精品成人| 亚洲中文av在线| 精品国产乱子伦一区二区三区| 美女高潮喷水抽搐中文字幕| 精品一区二区三区av网在线观看| 99精品欧美一区二区三区四区| av黄色大香蕉| 宅男免费午夜| 高潮久久久久久久久久久不卡| 国产91精品成人一区二区三区| 日韩欧美在线二视频| 亚洲片人在线观看| 日本在线视频免费播放| 丰满的人妻完整版| 亚洲人成网站高清观看| 亚洲av成人av| 看片在线看免费视频| 久久精品综合一区二区三区| 在线观看舔阴道视频| 国产成人福利小说| 亚洲av片天天在线观看| 免费看美女性在线毛片视频| www日本在线高清视频| 精品免费久久久久久久清纯| 亚洲色图av天堂| 天天躁日日操中文字幕| 国产精品亚洲一级av第二区| 男人和女人高潮做爰伦理| 国产午夜福利久久久久久| 成年人黄色毛片网站| 999精品在线视频| tocl精华| 国产乱人视频| 国产视频内射| 啦啦啦免费观看视频1| 亚洲av电影不卡..在线观看| 久久这里只有精品中国| 久久人妻av系列| 丰满人妻一区二区三区视频av | 欧美黄色淫秽网站| 一级a爱片免费观看的视频| 国产v大片淫在线免费观看| www日本黄色视频网| 99久国产av精品| 巨乳人妻的诱惑在线观看| 成人三级做爰电影| 一进一出好大好爽视频| 怎么达到女性高潮| 亚洲av电影在线进入| 搡老熟女国产l中国老女人| 制服丝袜大香蕉在线| 免费看光身美女| 91久久精品国产一区二区成人 | 国产爱豆传媒在线观看| 两人在一起打扑克的视频| 亚洲真实伦在线观看| 欧美色视频一区免费| 亚洲成av人片在线播放无| 天堂av国产一区二区熟女人妻| 久久婷婷人人爽人人干人人爱| 成人av在线播放网站| 亚洲国产高清在线一区二区三| 日本 欧美在线| 精品福利观看| 亚洲av第一区精品v没综合| 9191精品国产免费久久| 老汉色av国产亚洲站长工具| 亚洲天堂国产精品一区在线| 国产一区二区在线观看日韩 | cao死你这个sao货| 亚洲av成人一区二区三| 亚洲片人在线观看| 国产视频内射| 老汉色∧v一级毛片| 一夜夜www| 日日夜夜操网爽| 香蕉av资源在线| 首页视频小说图片口味搜索| 不卡一级毛片| 国产精品久久久久久人妻精品电影| 久久久久久久久中文| 国产精品,欧美在线| 免费在线观看成人毛片| 国产午夜福利久久久久久| 最近最新中文字幕大全免费视频| 欧美高清成人免费视频www| 日韩欧美三级三区| 蜜桃久久精品国产亚洲av| av中文乱码字幕在线| 精品欧美国产一区二区三| 又黄又爽又免费观看的视频| 国产精品爽爽va在线观看网站| 日本在线视频免费播放| 精品久久久久久,| 国内精品久久久久久久电影| 国产亚洲精品综合一区在线观看| 国产免费男女视频| 亚洲精品国产精品久久久不卡| av黄色大香蕉| 99re在线观看精品视频| 无人区码免费观看不卡| 国产亚洲av高清不卡| 午夜亚洲福利在线播放| 国产伦精品一区二区三区四那| 岛国视频午夜一区免费看| 老司机午夜福利在线观看视频| 97碰自拍视频| 午夜精品在线福利| 一级黄色大片毛片| 国产成人精品久久二区二区91| 一本综合久久免费| www日本在线高清视频| 国产精品日韩av在线免费观看| 人妻久久中文字幕网| 黑人欧美特级aaaaaa片| 99re在线观看精品视频| 老司机午夜十八禁免费视频| 免费在线观看影片大全网站| 男人舔女人的私密视频| 少妇裸体淫交视频免费看高清| 免费在线观看日本一区| 国产伦精品一区二区三区视频9 | 叶爱在线成人免费视频播放| 国产成人精品久久二区二区免费| 久久久水蜜桃国产精品网| 国产 一区 欧美 日韩| 国产精品久久久人人做人人爽| 精品午夜福利视频在线观看一区| 久久人人精品亚洲av| 非洲黑人性xxxx精品又粗又长| 国产精品综合久久久久久久免费| 欧美乱妇无乱码| 女同久久另类99精品国产91| 欧美xxxx黑人xx丫x性爽| 精品福利观看| 色播亚洲综合网| 精品日产1卡2卡| 无遮挡黄片免费观看| 97超级碰碰碰精品色视频在线观看| 男人舔奶头视频| 国产成人一区二区三区免费视频网站| 日本黄色视频三级网站网址| 国产精品久久久久久人妻精品电影| 一个人看的www免费观看视频| 香蕉国产在线看| 日本与韩国留学比较| 在线播放国产精品三级| 亚洲无线在线观看| 婷婷精品国产亚洲av| 别揉我奶头~嗯~啊~动态视频| 女人高潮潮喷娇喘18禁视频| 亚洲欧洲精品一区二区精品久久久| 国产黄a三级三级三级人| 搞女人的毛片| 亚洲成av人片免费观看| 精品久久久久久,| 最新美女视频免费是黄的| 噜噜噜噜噜久久久久久91| 婷婷亚洲欧美| 精品熟女少妇八av免费久了| 国产精品香港三级国产av潘金莲| 免费在线观看日本一区| 最近最新中文字幕大全免费视频| 三级国产精品欧美在线观看 | 久久中文看片网| netflix在线观看网站| 亚洲黑人精品在线| 18禁观看日本| 精品久久久久久成人av| 国产一区二区激情短视频| 色老头精品视频在线观看| 欧美色视频一区免费| 91av网一区二区| 天堂网av新在线| 男女之事视频高清在线观看| 色哟哟哟哟哟哟| 精品久久蜜臀av无| 99热只有精品国产| 特级一级黄色大片| 真人一进一出gif抽搐免费| 日韩成人在线观看一区二区三区| 久久久久久久久免费视频了| 免费观看人在逋| 成年女人看的毛片在线观看| av片东京热男人的天堂| 在线免费观看的www视频| 一本综合久久免费| 欧美乱妇无乱码| 丁香欧美五月| 一级毛片女人18水好多| 日本a在线网址| av在线蜜桃| 亚洲av电影不卡..在线观看| 女生性感内裤真人,穿戴方法视频| 亚洲午夜精品一区,二区,三区| 真人一进一出gif抽搐免费| 精华霜和精华液先用哪个| 欧美一级a爱片免费观看看| 日本a在线网址| 12—13女人毛片做爰片一| 精品午夜福利视频在线观看一区| 亚洲精品美女久久av网站| 激情在线观看视频在线高清| 国产成人av教育| 香蕉av资源在线| 国产亚洲精品一区二区www| 桃红色精品国产亚洲av| 国产成人系列免费观看| 亚洲 欧美一区二区三区| 色播亚洲综合网| 欧美+亚洲+日韩+国产| 日本三级黄在线观看| 亚洲狠狠婷婷综合久久图片| 一区二区三区激情视频| 欧美一级毛片孕妇| 久久久水蜜桃国产精品网| 网址你懂的国产日韩在线| 亚洲人成伊人成综合网2020| 久久中文看片网| 两性午夜刺激爽爽歪歪视频在线观看| 欧美色视频一区免费| 精品国产三级普通话版| 黑人操中国人逼视频| 这个男人来自地球电影免费观看| 午夜福利18| 欧美最黄视频在线播放免费| 啪啪无遮挡十八禁网站| 精品人妻1区二区| 久久热在线av| av视频在线观看入口| 伊人久久大香线蕉亚洲五| 午夜久久久久精精品| 男女之事视频高清在线观看| ponron亚洲| 亚洲欧洲精品一区二区精品久久久| 精品国产乱码久久久久久男人| 亚洲中文字幕日韩| 婷婷丁香在线五月| 九九热线精品视视频播放| 九九在线视频观看精品| 国产v大片淫在线免费观看| 亚洲中文av在线| 午夜两性在线视频| 精品午夜福利视频在线观看一区| 欧美日韩中文字幕国产精品一区二区三区| 欧美日本亚洲视频在线播放| 亚洲天堂国产精品一区在线| 男女之事视频高清在线观看| 大型黄色视频在线免费观看| 国产aⅴ精品一区二区三区波| 91在线精品国自产拍蜜月 | 99国产综合亚洲精品| 1000部很黄的大片| 成人18禁在线播放| 国内毛片毛片毛片毛片毛片| 国产高清视频在线播放一区| 久久久久精品国产欧美久久久| 性色av乱码一区二区三区2| 999久久久国产精品视频| 国产人伦9x9x在线观看| 亚洲七黄色美女视频| 此物有八面人人有两片| 国产成人av激情在线播放| 日本 欧美在线| 亚洲国产精品999在线| 一级毛片高清免费大全| 十八禁人妻一区二区| 国产又色又爽无遮挡免费看| 亚洲国产日韩欧美精品在线观看 | 真人做人爱边吃奶动态| 午夜福利视频1000在线观看|