個(gè)性化搜索引擎推薦算法研究
發(fā)布時(shí)間：2019-06-20 22:19
【摘要】： 隨著Internet和網(wǎng)絡(luò)信息技術(shù)的迅猛發(fā)展,網(wǎng)絡(luò)資源呈指數(shù)急劇增長,傳統(tǒng)的通用搜索引擎的查詢結(jié)果只依賴于查詢關(guān)鍵詞,而實(shí)際上,即便相同的查詢詞,不同的用戶查詢目的可能不同,所希望的返回結(jié)果也會因人而異。針對這種情況,人們迫切需要一種針對個(gè)人特點(diǎn)提供更加精確查詢結(jié)果的搜索工具,以用戶為中心的個(gè)性化搜索引擎便應(yīng)運(yùn)而生。 本文首先全面了解了實(shí)現(xiàn)個(gè)性化搜索引擎的基本理論和研究現(xiàn)狀,并對現(xiàn)有各種個(gè)性化推薦技術(shù)進(jìn)行性能對比分析,為以后的研究提供了理論基礎(chǔ)。 接著,本文研究了推薦領(lǐng)域最重要的協(xié)同過濾算法,基于用戶推薦的協(xié)同過濾可以為用戶發(fā)現(xiàn)新的潛在感興趣的資源,但是具有稀疏性等缺點(diǎn);基于項(xiàng)目推薦的協(xié)同過濾在某種程度上可以解決稀疏性,而且簡單有效,但是只能發(fā)現(xiàn)和用戶已有興趣相似的信息。針對這些問題,本文提出了一種基于單值分解的集影響協(xié)作過濾推薦算法,利用單值分解和增大影響集來提高協(xié)同過濾的推薦質(zhì)量,解決稀疏性問題,改善推薦系統(tǒng)的性能。 然而在應(yīng)用了改進(jìn)的協(xié)同過濾推薦算法的推薦系統(tǒng)中,除了已經(jīng)解決的稀疏性問題,還存在著冷開始新項(xiàng)目問題、擴(kuò)展性問題以及用戶潛在興趣難以挖掘等,本文在前面研究的基礎(chǔ)上,提出了一種個(gè)性化推薦融合算法,在優(yōu)秀的基于用戶協(xié)同過濾推薦思想基礎(chǔ)上,結(jié)合現(xiàn)有矩陣技術(shù),擴(kuò)展影響集,利用基于項(xiàng)目協(xié)同過濾以及基于內(nèi)容過濾,解決了稀疏問題、擴(kuò)展性問題、冷開始和用戶潛在興趣難以挖掘等問題,提高了推薦系統(tǒng)的推薦質(zhì)量。并在此基礎(chǔ)上,提出了一種策略預(yù)測用戶評分,解決了由于用戶對資源苛刻程度不同,而導(dǎo)致評分相差較大的問題。 最后,分析研究了開源全文檢索工具Lucene,并在該平臺上加入了個(gè)性化搜索模塊,分別對改進(jìn)的協(xié)作過濾推薦算法和個(gè)性化推薦融合算法進(jìn)行了仿真實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明:改進(jìn)的協(xié)作過濾推薦算法比傳統(tǒng)的協(xié)同過濾算法的推薦質(zhì)量高,而在冷開始狀況下,個(gè)性化推薦融合算法比改進(jìn)的協(xié)作過濾推薦算法推薦質(zhì)量高,預(yù)測評分更加與實(shí)際評分相接近,搜索結(jié)果更加符合用戶需求,提高了個(gè)性化搜索引擎的服務(wù)質(zhì)量。
[Abstract]:With the rapid development of Internet and network information technology, the network resources increase rapidly. The query results of the traditional general search engine only rely on query keywords. In fact, even if the same query words, different user query purposes may be different, the desired return results will vary from person to person. In view of this situation, people urgently need a search tool to provide more accurate query results according to personal characteristics, and a user-centered personalized search engine emerges as the times require. In this paper, the basic theory and research status of personalized search engine are fully understood, and the performance of various personalized recommendation technologies is compared and analyzed, which provides a theoretical basis for future research. Then, this paper studies the most important collaborative filtering algorithm in the field of recommendation. Collaborative filtering based on user recommendation can find new potentially interested resources for users, but it has some shortcomings, such as sparsity. Collaborative filtering based on project recommendation can solve sparsity to some extent, and is simple and effective, but can only find information similar to the interest of users. In order to solve these problems, a set-influence cooperative filtering recommendation algorithm based on single-valued decomposition is proposed in this paper. Single-valued decomposition and increasing influence set are used to improve the recommendation quality of collaborative filtering, solve the sparsity problem and improve the performance of recommendation system. However, in the recommendation system which applies the improved collaborative filtering recommendation algorithm, in addition to the sparsity problem that has been solved, there are also cold start new project problems, expansibility problems and difficult to mine the potential interest of users. On the basis of the previous research, this paper proposes a personalized recommendation fusion algorithm. On the basis of the excellent recommendation idea based on user collaborative filtering, combined with the existing matrix technology, the impact set is extended. By using project-based collaborative filtering and content-based filtering, the sparse problem, expansibility problem, cold start and difficult to mine the potential interest of users are solved, and the recommendation quality of recommendation system is improved. On this basis, a strategy is proposed to predict the user score, which solves the problem that the score is different because the user is harsh on the resource. Finally, the open source full-text retrieval tool Lucene, is analyzed and studied, and the personalized search module is added to the platform, and the improved collaborative filtering recommendation algorithm and personalized recommendation fusion algorithm are simulated respectively. The experimental results show that the recommendation quality of the improved collaborative filtering recommendation algorithm is higher than that of the traditional collaborative filtering algorithm, but at the beginning of cold, the personalized recommendation fusion algorithm has higher recommendation quality than the improved collaborative filtering recommendation algorithm, the prediction score is more close to the actual score, the search results are more in line with the needs of users, and the quality of service of personalized search engine is improved.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2009
【分類號】：TP391.3
本文編號：2503553
[Abstract]:With the rapid development of Internet and network information technology, the network resources increase rapidly. The query results of the traditional general search engine only rely on query keywords. In fact, even if the same query words, different user query purposes may be different, the desired return results will vary from person to person. In view of this situation, people urgently need a search tool to provide more accurate query results according to personal characteristics, and a user-centered personalized search engine emerges as the times require. In this paper, the basic theory and research status of personalized search engine are fully understood, and the performance of various personalized recommendation technologies is compared and analyzed, which provides a theoretical basis for future research. Then, this paper studies the most important collaborative filtering algorithm in the field of recommendation. Collaborative filtering based on user recommendation can find new potentially interested resources for users, but it has some shortcomings, such as sparsity. Collaborative filtering based on project recommendation can solve sparsity to some extent, and is simple and effective, but can only find information similar to the interest of users. In order to solve these problems, a set-influence cooperative filtering recommendation algorithm based on single-valued decomposition is proposed in this paper. Single-valued decomposition and increasing influence set are used to improve the recommendation quality of collaborative filtering, solve the sparsity problem and improve the performance of recommendation system. However, in the recommendation system which applies the improved collaborative filtering recommendation algorithm, in addition to the sparsity problem that has been solved, there are also cold start new project problems, expansibility problems and difficult to mine the potential interest of users. On the basis of the previous research, this paper proposes a personalized recommendation fusion algorithm. On the basis of the excellent recommendation idea based on user collaborative filtering, combined with the existing matrix technology, the impact set is extended. By using project-based collaborative filtering and content-based filtering, the sparse problem, expansibility problem, cold start and difficult to mine the potential interest of users are solved, and the recommendation quality of recommendation system is improved. On this basis, a strategy is proposed to predict the user score, which solves the problem that the score is different because the user is harsh on the resource. Finally, the open source full-text retrieval tool Lucene, is analyzed and studied, and the personalized search module is added to the platform, and the improved collaborative filtering recommendation algorithm and personalized recommendation fusion algorithm are simulated respectively. The experimental results show that the recommendation quality of the improved collaborative filtering recommendation algorithm is higher than that of the traditional collaborative filtering algorithm, but at the beginning of cold, the personalized recommendation fusion algorithm has higher recommendation quality than the improved collaborative filtering recommendation algorithm, the prediction score is more close to the actual score, the search results are more in line with the needs of users, and the quality of service of personalized search engine is improved.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2009
【分類號】：TP391.3
【引證文獻(xiàn)】
相關(guān)期刊論文 前1條
1 袁莉萍;;專業(yè)化音樂教學(xué)信息搜索引擎技術(shù)研究[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2011年05期
相關(guān)碩士學(xué)位論文 前4條
1 白曉波;基于事件驅(qū)動模型的搜索引擎的研究及原型系統(tǒng)設(shè)計(jì)[D];湖南大學(xué);2010年
2 代旭峰;基于用戶興趣模型的搜索引擎結(jié)果推薦系統(tǒng)[D];復(fù)旦大學(xué);2011年
3 譚明輝;基于web數(shù)據(jù)挖掘的個(gè)性化搜索引擎的研究與應(yīng)用[D];江西農(nóng)業(yè)大學(xué);2012年
4 文新勝;基于專家池的協(xié)同過濾推薦系統(tǒng)研究[D];湖北大學(xué);2011年
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