本文選題：協(xié)同過(guò)濾 + 用戶行為序列　； 參考：《南京大學(xué)》2012年碩士論文

【摘要】：隨著Internet迅速普及,如何從浩如煙海的互聯(lián)網(wǎng)數(shù)據(jù)中迅速找到相關(guān)信息,是互聯(lián)網(wǎng)用戶面臨的重要問(wèn)題,也是互聯(lián)網(wǎng)技術(shù)研究的重點(diǎn)之一。目前,搜索引擎和信息過(guò)濾是解決該問(wèn)題最常用到的兩種主要技術(shù)。 個(gè)性化推薦是一種新興的信息過(guò)濾技術(shù)。它從用戶的歷史行為數(shù)據(jù)中發(fā)現(xiàn)用戶的興趣偏好,采用“推送”的方式,將用戶感興趣的信息從大量數(shù)據(jù)中過(guò)濾出來(lái),并根據(jù)用戶對(duì)信息“感興趣”的程度,按一定的方式將相關(guān)信息呈現(xiàn)在用戶面前。對(duì)于電子商務(wù)平臺(tái)而言,使用個(gè)性化推薦技術(shù),有助于提升平臺(tái)的“長(zhǎng)尾”優(yōu)勢(shì),增加利益攸關(guān)方的收益。 本文將個(gè)性化推薦相關(guān)技術(shù)引入“搜房網(wǎng)”垂直搜索引擎升級(jí)的設(shè)計(jì)中,分析歷史用戶的操作行為,提取其的興趣模型,使用基于用戶協(xié)同的過(guò)濾方式,發(fā)現(xiàn)當(dāng)前用戶興趣,在項(xiàng)目庫(kù)中找出當(dāng)前用戶可能感興趣的信息并將之推薦給當(dāng)前用戶,緩解垂直搜索引擎面臨的“過(guò)度篩選”問(wèn)題。本文主要工作如下： 概述了個(gè)性化推薦領(lǐng)域的經(jīng)典算法、理論、研究熱點(diǎn)及相關(guān)技術(shù),比較了基于規(guī)則發(fā)現(xiàn)、基于內(nèi)容過(guò)濾和基于協(xié)同過(guò)濾等相關(guān)算法和理論的優(yōu)缺點(diǎn),并闡述了它們各自的應(yīng)用場(chǎng)景。同時(shí)還簡(jiǎn)要介紹了隱馬爾可夫模型的相關(guān)理論。 基于“搜房網(wǎng)”搜索引擎的用戶行為特點(diǎn),分析了搜索引擎系統(tǒng)的用戶搜索行為日志,從而給出了用戶行為、用戶行為序列的相關(guān)定義。設(shè)計(jì)了一個(gè)序列融合算法,提取日志中的用戶行為序列,同時(shí),提出了一種計(jì)算用戶行為序列相似度的方法。 根據(jù)用戶行為序列對(duì)用戶進(jìn)行了建模,并基于隱馬爾可夫模型理論,設(shè)計(jì)了預(yù)測(cè)用戶行為序列的模型及模型參數(shù)的估計(jì)方法。進(jìn)而設(shè)計(jì)了一套基于用戶行為序列分析,綜合考慮了用戶協(xié)同、用戶行為序列相似性、項(xiàng)目時(shí)效性等因素的項(xiàng)目推薦算法。此外,還制定了相關(guān)的“冷啟動(dòng)”策略。 最后,結(jié)合“搜房網(wǎng)”的實(shí)際需求,設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)房屋信息個(gè)性化推薦系統(tǒng)。設(shè)計(jì)相關(guān)實(shí)驗(yàn),在真實(shí)的數(shù)據(jù)集上,驗(yàn)證了系統(tǒng)的用戶行為預(yù)測(cè)效果,結(jié)合隱馬爾可夫模型特點(diǎn),分析了系統(tǒng)關(guān)于用戶行為預(yù)測(cè)設(shè)計(jì)上的一些局限性。并結(jié)合系統(tǒng)特性,討論了評(píng)價(jià)推薦項(xiàng)目相關(guān)性和推薦列表排序正確性的相關(guān)指標(biāo)。設(shè)計(jì)實(shí)驗(yàn),評(píng)估系統(tǒng)在推薦列表排序、推薦項(xiàng)目相關(guān)性等方面的實(shí)際效果,并在此基礎(chǔ)上分析了系統(tǒng)設(shè)計(jì)的不足,對(duì)系統(tǒng)的下一步工作進(jìn)行了展望。
[Abstract]:With the rapid popularization of Internet, how to quickly find the relevant information from the vast amount of Internet data is an important problem facing Internet users, and also one of the key points in the research of Internet technology.At present, search engine and information filtering are the two most commonly used technologies to solve this problem.Personalized recommendation is a new information filtering technology.It finds the user's interest preference from the user's historical behavior data, filters out the information of the user's interest from a large amount of data by "pushing" the information, and according to the degree of the user's "interest" in the information,The relevant information is presented to the user in a certain way.For e-commerce platform, the use of personalized recommendation technology will help to enhance the platform's "long tail" advantage and increase the benefits of stakeholders.This paper introduces the personalized recommendation technology into the design of vertical search engine upgrade, analyzes the operation behavior of historical users, extracts its interest model, and finds out the current user's interest by using the filtering method based on user cooperation.Find out the information that the current user may be interested in in the project library and recommend it to the current user to alleviate the problem of "excessive filtering" faced by the vertical search engine.The main work of this paper is as follows:This paper summarizes the classical algorithms, theories, research hotspots and related technologies in the field of personalized recommendation, and compares the advantages and disadvantages of the algorithms and theories related to rule-based discovery, content-based filtering and collaborative filtering.Their respective application scenarios are described.At the same time, the theory of hidden Markov model is briefly introduced.Based on the characteristics of user behavior in search engine, the user search behavior log of search engine system is analyzed, and the relevant definitions of user behavior and user behavior sequence are given.A sequence fusion algorithm is designed to extract user behavior sequences from logs and a method to calculate the similarity of user behavior sequences is proposed.The user is modeled according to the user behavior sequence, and based on the hidden Markov model theory, the model of predicting user behavior sequence and the estimation method of model parameters are designed.Then, a set of project recommendation algorithm based on user behavior sequence analysis is designed, which considers the factors of user collaboration, user behavior sequence similarity, project timeliness and so on.In addition, the related "cold start" strategy has been developed.Finally, a personalized recommendation system for building information is designed and implemented in combination with the actual demand of Sou Fang net.Experiments are designed to verify the effectiveness of user behavior prediction in real data sets. Combined with the characteristics of hidden Markov model, some limitations of user behavior prediction design are analyzed.Combined with the characteristics of the system, this paper discusses the related indexes of evaluating the relevance of recommendation items and the correctness of recommendation list ranking.Design experiments, evaluate the system in the recommended list ranking, recommendation item correlation and other aspects of the actual results, and on the basis of the analysis of the system design deficiencies, the next work of the system is prospected.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 郭靜;移動(dòng)信息采集分析軟件的設(shè)計(jì)與實(shí)現(xiàn)[D];解放軍信息工程大學(xué);2012年

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本文編號(hào)：1766704