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  本文選題：互動電視　切入點：推薦系統(tǒng)　出處：《復(fù)旦大學(xué)》2012年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)、廣播電視網(wǎng)絡(luò)的不斷發(fā)展,網(wǎng)絡(luò)上的信息不斷地增加。尤其是在WEB2.0技術(shù)的迅猛發(fā)展之下,互聯(lián)網(wǎng)已經(jīng)成為全球最大的信息庫,它給我們帶來極大便利的同時,也給帶來了信息膨脹的問題。作為我國信息產(chǎn)業(yè)發(fā)展的戰(zhàn)略目標(biāo)(“三網(wǎng)融合”)正不斷地改變著我們的生活。隨著2010年國務(wù)院頒布《加快發(fā)展三網(wǎng)融合發(fā)展》的政策,最近幾年下一代廣播電視網(wǎng)絡(luò)得到了快速的發(fā)展,具有互動點播功能的機(jī)頂盒走進(jìn)了我們的生活,可供用戶點播的視頻也越來越多,電視用戶獲得了互聯(lián)互通、個性化搜索、個性化推薦等全方面的服務(wù)。 隨著數(shù)字電視網(wǎng)絡(luò)中中可供用戶點播的視頻數(shù)量越來越多,在數(shù)以萬計的視頻面前,用戶感到迷茫,搜索引擎也只能解決一小部分的問題。所以而向互動電視的個性化推薦系統(tǒng)逐漸成為廣大學(xué)者的研究重點。 影視節(jié)目搜索點播是互動電視的一個重要功能,隨著影視節(jié)目數(shù)量的日益增多,內(nèi)容日益復(fù)雜,用戶越來越對挑選節(jié)目感到疑惑,視頻推薦技術(shù)可以作為一種理想的解決方案。在用戶歷史播放記錄的基礎(chǔ)上,通過分析用戶的行為和喜好,再根據(jù)互動電視特有的信息(如多用戶共用機(jī)頂盒、節(jié)目的時間特征),最后為用戶產(chǎn)生一組推薦。本文主要研究了互動電視點播系統(tǒng)中的影視節(jié)目推薦技術(shù),并且實現(xiàn)了一套面向互動電視的個性化推薦系統(tǒng)。該系統(tǒng)通過分析影視節(jié)目之間的關(guān)系和用戶的歷史記錄,挖掘用戶之間的相似度和偏好,進(jìn)而給用戶推薦一組個性化的影視節(jié)目列表,減少了用戶選擇視頻的時間,提升了用戶的體驗。
[Abstract]:With the continuous development of the Internet and radio and television networks, the information on the network is constantly increasing. Especially with the rapid development of WEB2.0 technology, the Internet has become the largest information base in the world, which brings us great convenience at the same time. It has also brought the problem of information inflation. As a strategic goal of the development of our information industry ("three networks convergence"), it is constantly changing our lives. With the promulgation of the policy of "accelerating the Development of three Networks Integration" by the State Council in 2010, In recent years, the next generation radio and television network has been developing rapidly. The set-top box with interactive on-demand function has come into our life, and more and more videos can be delivered to users on demand. Television users have gained connectivity and personalized search. Personalized recommendation and other services. As more and more videos are available to users on demand in the digital television network, users feel confused in the face of tens of thousands of videos. Search engine can only solve a small part of the problem, so the personalized recommendation system to interactive television has gradually become the research focus of the majority of scholars. Video program search on demand is an important function of interactive television. With the increasing number of TV programs and the increasing complexity of the content, users are more and more confused about the selection of programs. Video recommendation technology can be used as an ideal solution. Based on the history of users, by analyzing the behavior and preferences of users, and based on the specific information of interactive TV (such as multi-user sharing set-top box, etc.), The time feature of the program, and finally a group of recommendations for the user. This paper mainly studies the technology of the video program recommendation in the interactive television on demand system. A personalized recommendation system for interactive TV is implemented, which analyzes the relationship between TV programs and users' historical records, and excavates the similarity and preference between users. Then we recommend a set of personalized TV program list to users, which reduces the time for users to choose video, and improves the user's experience.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP391.3

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本文編號：1678731