【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)的廣泛普及和旅游者對(duì)旅游服務(wù)品質(zhì)的要求不斷加深,在線旅游、移動(dòng)旅游等服務(wù)也逐漸興起,在線旅游景點(diǎn)的個(gè)性化推薦逐漸成為個(gè)性化推薦技術(shù)領(lǐng)域的一個(gè)應(yīng)用和研究熱點(diǎn)。面對(duì)龐大、復(fù)雜的旅游數(shù)據(jù),旅游者對(duì)于旅游景點(diǎn)的個(gè)性化服務(wù)的需求也越來(lái)越強(qiáng)烈,研究高效、準(zhǔn)確的個(gè)性化旅游景點(diǎn)推薦系統(tǒng)具有很好的應(yīng)用價(jià)值。本文針對(duì)個(gè)性化旅游景點(diǎn)推薦的應(yīng)用需求,借助于社交網(wǎng)絡(luò)與貝葉斯網(wǎng)絡(luò),充分挖掘用戶與景點(diǎn)之間的匹配度進(jìn)行個(gè)性推薦。本文主要研究工作如下:(1)提出基于社交網(wǎng)絡(luò)的個(gè)性化旅游景點(diǎn)推薦算法。為了提高旅游景點(diǎn)推薦的準(zhǔn)確率,解決新用戶冷啟動(dòng)問(wèn)題,該算法將社交網(wǎng)絡(luò)因子加入到旅游景點(diǎn)推薦中,充分挖掘用戶間的社會(huì)化網(wǎng)絡(luò)關(guān)系。該算法處理過(guò)程如下:首先,采用耦合雙向聚類算法對(duì)用戶進(jìn)行聚類處理;然后,使用DBSCAN算法對(duì)景點(diǎn)聚類;最后將兩個(gè)穩(wěn)定的用戶集合和景點(diǎn)集合應(yīng)用在個(gè)性化推薦算法上,預(yù)測(cè)用戶下一個(gè)即將去的景點(diǎn)。在數(shù)據(jù)集上將該算法與一些傳統(tǒng)的算法進(jìn)行了對(duì)比實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明本文提出的算法具有較高的推薦準(zhǔn)確率。(2)為了量化旅游景點(diǎn)推薦,本文提出基于貝葉斯網(wǎng)絡(luò)學(xué)習(xí)的個(gè)性化旅游景點(diǎn)推薦算法。該算法為了解決新用戶與新景點(diǎn)的問(wèn)題,綜合使用了用戶的人口統(tǒng)計(jì)學(xué)信息,用戶-景點(diǎn)評(píng)分信息以及景點(diǎn)屬性。具體地,該算法首先使用傳統(tǒng)的協(xié)同過(guò)濾算法處理用戶屬性相似度與用戶行為相似度,使用基于內(nèi)容的算法處理景點(diǎn)間關(guān)系;然后,使用貝葉斯概率模型計(jì)算出用戶訪問(wèn)每個(gè)景點(diǎn)的概率;最后,將此算法在攜程網(wǎng)數(shù)據(jù)集上與傳統(tǒng)的算法進(jìn)行實(shí)驗(yàn)驗(yàn)證,結(jié)果表明該算法在處理新用戶和新景點(diǎn)問(wèn)題上具有更好的性能。
[Abstract]:With the widespread popularity of the mobile Internet and the deepening requirements of tourists for the quality of tourism services, online tourism, mobile tourism and other services are also gradually rising. Personalized recommendation of online tourist attractions has gradually become an application and research hotspot in the field of personalized recommendation technology. In the face of the huge and complicated tourism data, tourists' demand for personalized tourist attractions service is becoming more and more intense. Therefore, the study of highly efficient and accurate personalized recommendation system of tourist attractions has a good application value. This paper aims at the application demand of personalized tourist attraction recommendation, with the help of social network and Bayesian network, fully excavates the matching degree between user and scenic spot to carry on personality recommendation. The main research work of this paper is as follows: (1) A social-networking based personalized recommendation algorithm for tourist attractions is proposed. In order to improve the accuracy of recommendation of tourist attractions and solve the cold start problem of new users, the algorithm adds the social network factor to the recommendation of tourist attractions, and fully excavates the social network relationship among users. The processing process of the algorithm is as follows: firstly, the coupled bidirectional clustering algorithm is used to cluster the users; then, the DBSCAN algorithm is used to cluster the scenic spots. Finally, two stable user sets and attraction sets are applied to the personalized recommendation algorithm to predict the next destination that users will go to. The experimental results show that the proposed algorithm has a high recommendation accuracy. (2) in order to quantify the recommendation of tourist attractions, the proposed algorithm is compared with some traditional algorithms. This paper proposes a personalized recommendation algorithm for tourist attractions based on Bayesian network learning. In order to solve the problem of new users and new scenic spots, the algorithm uses the demographic information of users, the information of user-scenic spot score and the properties of scenic spots. Firstly, the traditional collaborative filtering algorithm is used to deal with the similarity between user attributes and user behavior, and the content-based algorithm is used to deal with the relationship between scenic spots. Then, the Bayesian probability model is used to calculate the probability of user visiting each scenic spot. Finally, the algorithm is tested on Ctrip data set with the traditional algorithm. The results show that the algorithm has better performance in dealing with new users and new scenic spots.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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