發(fā)布時(shí)間：2018-06-19 01:38

  本文選題：推薦系統(tǒng) + 酒店搜索　； 參考：《華中科技大學(xué)》2013年碩士論文

【摘要】：隨著信息技術(shù)和互聯(lián)網(wǎng)的發(fā)展，人們從信息匱乏時(shí)代來(lái)到了信息過(guò)載時(shí)代，用戶(hù)很難從海量的信息中快速獲得對(duì)自己有用的信息，對(duì)信息的利用率反而下降了。因此過(guò)濾信息的能力成為了衡量一個(gè)信息系統(tǒng)好壞的重要指標(biāo)。一個(gè)具好的信息系統(tǒng)，會(huì)從海量信息中過(guò)濾出用戶(hù)最關(guān)注的信息，這將大大增加系統(tǒng)工作的效率，并節(jié)省用戶(hù)尋找信息的時(shí)間。推薦系統(tǒng)正是在這種背景下應(yīng)運(yùn)而生，，作為傳統(tǒng)搜索引擎的一個(gè)補(bǔ)充，在解決信息過(guò)載問(wèn)題中發(fā)揮著重要的作用。 以某旅游垂直搜索網(wǎng)站為實(shí)例展開(kāi)面向酒店搜索的推薦技術(shù)研究。在深入分析了各種常用推薦系統(tǒng)后，結(jié)合酒店搜索的特點(diǎn)，設(shè)計(jì)了一種基于酒店相似度的酒店推薦系統(tǒng)。系統(tǒng)的設(shè)計(jì)思路是根據(jù)用戶(hù)最近的訪(fǎng)問(wèn)酒店推測(cè)出用戶(hù)的興趣，然后推薦相似的酒店。系統(tǒng)包括離線(xiàn)模塊和線(xiàn)上模塊，離線(xiàn)模塊根據(jù)點(diǎn)擊日志和酒店信息計(jì)算酒店相似性表，線(xiàn)上模塊根據(jù)用戶(hù)的最近訪(fǎng)問(wèn)歷史計(jì)算出推薦結(jié)果并負(fù)責(zé)收集用戶(hù)反饋和記錄系統(tǒng)狀態(tài)。為了對(duì)系統(tǒng)進(jìn)行離線(xiàn)評(píng)測(cè)和研究，同時(shí)設(shè)計(jì)了一種基于用戶(hù)訪(fǎng)問(wèn)時(shí)間序列的推薦評(píng)測(cè)系統(tǒng)，并定義了命中率和命中率精度兩個(gè)精確度指標(biāo)作為主要的評(píng)測(cè)指標(biāo)。該評(píng)測(cè)系統(tǒng)把每個(gè)用戶(hù)的點(diǎn)擊詳情日志看成訪(fǎng)問(wèn)序列，用最近訪(fǎng)問(wèn)歷史、當(dāng)前訪(fǎng)問(wèn)酒店和目標(biāo)酒店組成的時(shí)間窗口在訪(fǎng)問(wèn)序列上滑動(dòng)來(lái)模擬回放用戶(hù)的訪(fǎng)問(wèn)和推薦過(guò)程，并進(jìn)行相關(guān)統(tǒng)計(jì)，計(jì)算出評(píng)測(cè)指標(biāo)。該評(píng)測(cè)系統(tǒng)被用來(lái)研究基于內(nèi)容、協(xié)同過(guò)濾等多種相似性算法對(duì)系統(tǒng)的影響，并探究影響推薦效果的各種因素和改進(jìn)系統(tǒng)的方法。 經(jīng)過(guò)研究，發(fā)現(xiàn)使用基于協(xié)同過(guò)濾的Amazon相似性算法和點(diǎn)擊詳情轉(zhuǎn)化率相似性算法的效果最好，歸一化相似性是必要的，應(yīng)該經(jīng)常更新酒店相似性表。使用最佳訓(xùn)練集長(zhǎng)度、過(guò)濾壞數(shù)據(jù)、組合使用多推薦引擎可以有效改進(jìn)系統(tǒng)效果。綜合使用這些改進(jìn)方法之后，相對(duì)于原始系統(tǒng)，命中率提高了7%，命中率精度提高了15%。
[Abstract]:With the development of information technology and Internet, people come to the age of information overload from the age of lack of information. It is very difficult for users to obtain useful information quickly from the mass of information, but the utilization rate of information has declined. Therefore, the ability to filter information has become an important index to measure the quality of an information system. A good information system will filter out the most concerned information from the mass of information, which will greatly increase the efficiency of the system and save the time for users to find information. Recommendation system emerges as the times require under this background, as a supplement of traditional search engine, it plays an important role in solving the problem of information overload. Taking a vertical search website as an example, the recommendation technology for hotel search is studied. A hotel recommendation system based on hotel similarity is designed based on the analysis of various commonly used recommendation systems and the characteristics of hotel search. The design idea of the system is to speculate the user's interest based on the user's recent visit to the hotel, and then recommend similar hotel. The system includes offline module and online module. The offline module calculates hotel similarity table according to the click log and hotel information. The online module calculates the recommended results according to the user's recent visit history and is responsible for collecting user feedback and recording system status. In order to evaluate and study the system off-line, a recommendation evaluation system based on user access time series is designed, and the accuracy index of hit ratio and hit rate is defined as the main evaluation index. The system regards each user's click details log as an access sequence, and uses the recent access history, the time window composed of the current visiting hotel and the target hotel to slide on the access sequence to simulate the playback user's access and recommendation process. And carries on the correlation statistics, calculates the appraisal index. The evaluation system is used to study the influence of content-based, collaborative filtering and other similarity algorithms on the system, and to explore the factors that affect the effectiveness of recommendation and the methods to improve the system. It is found that the similarity algorithm of Amazon based on collaborative filtering and the similarity algorithm of conversion rate of click details are the best. The normalized similarity is necessary and the hotel similarity table should be updated frequently. Using the best training set length, filtering bad data and combining multiple recommendation engines can effectively improve the system effect. After using these improved methods, the hit ratio and accuracy of the original system are increased by 7% and 15% respectively.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP391.3

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