本文選題：足球視頻 + 視頻檢索�。� 參考：《華中科技大學(xué)》2012年碩士論文

【摘要】：互聯(lián)網(wǎng)信息量的急速增長(zhǎng)使得人們淹沒(méi)在信息的海洋中。盡管搜索引擎為用戶(hù)提供了便捷的信息檢索服務(wù)，但搜索引擎召回的成千上萬(wàn)的結(jié)果仍需要人們花費(fèi)很大的精力與時(shí)間去瀏覽符合自身興趣的信息。因此，從用戶(hù)與搜索引擎的交互信息中挖掘用戶(hù)偏好并為用戶(hù)提供個(gè)性化的檢索服務(wù)具有十分重要的意義。 在系統(tǒng)分析了偏好挖掘的國(guó)內(nèi)外研究現(xiàn)狀的基礎(chǔ)上，綜合用戶(hù)的顯式反饋信息和隱式反饋信息實(shí)現(xiàn)了基于足球領(lǐng)域信息的用戶(hù)偏好信息挖掘。針對(duì)偏好挖掘的實(shí)時(shí)性要求，通過(guò)對(duì)用戶(hù)的檢索信息進(jìn)行語(yǔ)義分析確定用戶(hù)檢索會(huì)話(huà)的邊界，以會(huì)話(huà)為單位獲取隱式反饋信息為偏好挖掘提供實(shí)時(shí)的用戶(hù)行為數(shù)據(jù)。對(duì)用戶(hù)反饋信息進(jìn)行分析，提取其中的偏好標(biāo)簽和偏好動(dòng)作并將其描述為標(biāo)簽權(quán)重有向圖，為偏好模型構(gòu)的建提供數(shù)據(jù)�；谧闱蝾I(lǐng)域知識(shí)設(shè)計(jì)了分層權(quán)重?zé)o向圖用戶(hù)偏好模型，為用戶(hù)偏好建模奠定基礎(chǔ)�？紤]到不同的偏好動(dòng)作所代表的喜好程度不一樣，對(duì)不同的偏好動(dòng)作賦予不同的權(quán)重。結(jié)合歷史偏好信息進(jìn)行實(shí)時(shí)偏好挖掘并引入了時(shí)間衰減因子，將當(dāng)前未出現(xiàn)的偏好信息的權(quán)值進(jìn)行衰減，描述用戶(hù)偏好的變化過(guò)程。將偏好挖掘算法應(yīng)用于搜球網(wǎng)，為搜球網(wǎng)用戶(hù)提供個(gè)性化的視頻檢索與視頻推薦服務(wù)。 實(shí)驗(yàn)結(jié)果表明，基于分層權(quán)重?zé)o向圖模型的偏好挖掘算法能很好地從用戶(hù)反饋信息中發(fā)掘用戶(hù)的長(zhǎng)期、中期以及短期的偏好信息。相比于原始的檢索系統(tǒng)，基于用戶(hù)偏好的個(gè)性化檢索結(jié)果排序和視頻推薦中起到了很好的效果，提高了搜球網(wǎng)的用戶(hù)體驗(yàn)。但系統(tǒng)目前僅僅考慮文本查詢(xún)信息，，尚未考慮用戶(hù)提交的檢索圖片這一偏好信息來(lái)源。同時(shí)在偏好分析時(shí)未考慮偏好標(biāo)簽的修飾詞對(duì)偏好挖掘的作用與影響。這兩方面的內(nèi)容將是未來(lái)研究的重點(diǎn)。
[Abstract]:The rapid growth of Internet information makes people submerged in the ocean of information. Although search engines provide users with convenient information retrieval services, the tens of thousands of results recalled by search engines still require people to spend a lot of energy and time to browse information that is in line with their own interests. Therefore, it is of great significance to mine user preferences from the interactive information between users and search engines and to provide personalized retrieval services for users. Based on the systematic analysis of the current research situation of preference mining at home and abroad, the user preference information mining based on football domain information is realized by integrating the explicit feedback information and implicit feedback information of users. According to the real-time requirement of preference mining, the boundary of user retrieval session is determined by semantic analysis of user's retrieval information, and real-time user behavior data is provided for preference mining with implicit feedback information obtained by session. The user feedback information is analyzed and the preference tags and preference actions are extracted and described as label weight digraphs to provide data for the construction of preference models. Based on football domain knowledge, a hierarchical weight undirected graph user preference model is designed, which lays a foundation for user preference modeling. Considering that different preference actions represent different degrees of preference, different preference actions are given different weights. This paper combines historical preference information with real-time preference mining and introduces time decay factor to attenuate the weights of current preference information and describe the process of user preference change. The preference mining algorithm is applied to the search net to provide personalized video retrieval and video recommendation services for the users. The experimental results show that the preference mining algorithm based on delamination weight undirected graph model can well extract the long-term, medium-term and short-term preference information from user feedback information. Compared with the original retrieval system, personalized retrieval result ranking and video recommendation based on user preference play a good role in improving the user experience. However, at present, the system only considers the text query information, and does not consider the user submitted image retrieval as a preferred information source. At the same time, the effect of preference tag modifier on preference mining is not considered in preference analysis. These two aspects of the content will be the focus of future research.
【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】：2012
【分類(lèi)號(hào)】：TP391.3

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