本文關(guān)鍵詞： 問(wèn)答系統(tǒng) 電子商務(wù) 相似度 關(guān)鍵詞 意圖　出處：《五邑大學(xué)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：“中國(guó)制造2025”中提出要加快發(fā)展智能制造裝備和產(chǎn)品,作為其中重要部分的服務(wù)機(jī)器人以及智能家居都倍受產(chǎn)業(yè)界追捧。在工業(yè)機(jī)器人之后,服務(wù)機(jī)器人也將得到政策支持,獲得政策頂層設(shè)計(jì)規(guī)劃。經(jīng)過(guò)多年的發(fā)展我國(guó)的電子商務(wù)市場(chǎng)規(guī)模大,網(wǎng)絡(luò)購(gòu)物用戶(hù)基數(shù)多,用戶(hù)對(duì)商品的體驗(yàn)不再停留在產(chǎn)品質(zhì)量,對(duì)服務(wù)質(zhì)量的要求不斷提高;另外,客服人員服務(wù)成本不斷提升、流失率高以及招聘專(zhuān)業(yè)客服難度大等問(wèn)題突出,電商客服機(jī)器人是人工智能的新形式,它將更高效地服務(wù)各商家和用戶(hù)�？头藛T以及電商客服機(jī)器人在和用戶(hù)交流的過(guò)程中,關(guān)鍵環(huán)節(jié)在對(duì)用戶(hù)意圖的識(shí)別,只有準(zhǔn)確發(fā)現(xiàn)用戶(hù)的意圖所在才能有效地為其服務(wù),不斷提高用戶(hù)的滿(mǎn)意進(jìn)而獲得忠誠(chéng)的用戶(hù)實(shí)現(xiàn)盈利。本文對(duì)電商客服自動(dòng)問(wèn)答系統(tǒng)(電商客服機(jī)器人)所屬的問(wèn)答系研究統(tǒng)進(jìn)行了梳理,介紹了問(wèn)答系統(tǒng)的發(fā)展以及系統(tǒng)的類(lèi)型、結(jié)構(gòu)等基礎(chǔ)知識(shí);同時(shí),了解了問(wèn)答系統(tǒng)涉及的關(guān)鍵詞提取、詞語(yǔ)相似度計(jì)算等技術(shù),此外,深入理解了BP神經(jīng)網(wǎng)絡(luò)算法。在以上知識(shí)準(zhǔn)備基礎(chǔ)上,本文基于電商客服機(jī)器人系統(tǒng),首先,在對(duì)用戶(hù)語(yǔ)句處理上使用了中科院分詞技術(shù),并構(gòu)建了化妝品領(lǐng)域所需的專(zhuān)業(yè)詞典來(lái)提高分詞準(zhǔn)確性,詞典涉及商品、美妝、護(hù)膚等多方面;根據(jù)網(wǎng)絡(luò)購(gòu)物語(yǔ)言特點(diǎn),對(duì)用戶(hù)溝通語(yǔ)料進(jìn)行統(tǒng)計(jì)分析篩選建立了相應(yīng)的停用詞表;其次,選取語(yǔ)義、自身、位置三大塊特征值信息利用BP神經(jīng)網(wǎng)絡(luò)模型對(duì)用戶(hù)語(yǔ)句進(jìn)行關(guān)鍵詞提取。然后,本文構(gòu)建了網(wǎng)絡(luò)購(gòu)物中用戶(hù)比較關(guān)注的商品和服務(wù)意圖網(wǎng)絡(luò),這一網(wǎng)絡(luò)作為每一個(gè)用戶(hù)的描述畫(huà)像,為系統(tǒng)后期回答服務(wù)。最后,依據(jù)艾賓浩斯的人類(lèi)遺忘規(guī)律,結(jié)合用戶(hù)與客服機(jī)器人溝通的時(shí)間特點(diǎn),基于遺忘曲線(xiàn)構(gòu)建了“單階段”和“多階段”用戶(hù)商品意圖模型,實(shí)現(xiàn)用戶(hù)的商品意圖強(qiáng)度描述,通過(guò)提取的關(guān)鍵詞利用Word2vec語(yǔ)義分析工具計(jì)算用戶(hù)商品意圖強(qiáng)度,對(duì)問(wèn)答系統(tǒng)回答準(zhǔn)確有一定改善,用戶(hù)商品意圖強(qiáng)度的獲得還能給用戶(hù)進(jìn)行個(gè)性化推薦以及相關(guān)服務(wù)做指導(dǎo)。
[Abstract]:Service robots and smart homes, which are an important part of the "made in China 2025" initiative to speed up the development of intelligent manufacturing equipment and products, are popular in industry. After industrial robots, service robots will also receive policy support. After many years of development, the e-commerce market in China has a large scale, a large base of online shopping users, and the user's experience of goods no longer stays in the product quality, and the demand for service quality is constantly improved. The cost of customer service is constantly rising, the loss rate is high, and the difficulty of recruiting professional customer service is very serious. The e-business customer service robot is a new form of artificial intelligence. It will serve merchants and users more efficiently. In the process of communicating with customers, the key link of the customer service robot is the identification of the user's intention. Only by finding out exactly where the user's intention is, can it be served effectively. In this paper, the research system of the question and answer system, which belongs to the electronic customer service automatic question answering system (e-business customer service robot), is combed. This paper introduces the development of the question and answer system, the basic knowledge of the system, such as the type and structure of the system, and the key words extraction, word similarity calculation and other techniques involved in the question and answer system. Deeply understand BP neural network algorithm. Based on the above knowledge preparation, this paper based on the e-commerce customer service robot system, first of all, the use of Chinese Academy of Sciences word segmentation technology in the processing of user statements, The specialized dictionary needed in cosmetics field is constructed to improve the accuracy of word segmentation. The dictionary involves commodities, makeup, skin care and so on, according to the language characteristics of online shopping, The corresponding stop word list is established by statistical analysis and screening of the user communication corpus. Secondly, three blocks of eigenvalue information, namely, semantic, self and position, are selected to extract the key words of user statements by BP neural network model. This paper constructs a network of goods and service intentions that users pay more attention to in online shopping. This network, as a descriptive portrait of each user, serves for the later period of the system. Finally, according to the law of human forgetting, According to the time characteristics of the communication between the user and the customer service robot, a "single-stage" and "multi-stage" user's merchandise intention model is constructed based on the forgetting curve to describe the intensity of the user's commodity intention. The extracted keywords use Word2vec semantic analysis tools to calculate the strength of the user's commodity intention and improve the accuracy of the answer of the question and answer system. The acquisition of the intensity of the user's commodity intention can also provide users with personalized recommendation and relevant services to guide them.
【學(xué)位授予單位】：五邑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP242;TP391.1
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本文編號(hào)：1540545