本文選題：協(xié)同過濾 + 新聞特點(diǎn)��； 參考：《重慶理工大學(xué)》2017年碩士論文

【摘要】：個(gè)性化新聞推薦系統(tǒng)是根據(jù)每個(gè)登錄過推薦系統(tǒng)的用戶的歷史行為,使用推薦算法為每個(gè)用戶推薦其感興趣的新聞�；趨f(xié)同過濾算法的個(gè)性化新聞推薦算法是根據(jù)用戶的歷史行為計(jì)算新聞的相似度,并完成相似新聞的推薦。這種相似度的計(jì)算方法沒有挖掘新聞本身的特點(diǎn),存在數(shù)據(jù)稀疏的問題。同時(shí),協(xié)同過濾算法沒有考慮用戶的興趣隨時(shí)間發(fā)生動(dòng)態(tài)變化的問題。針對(duì)推薦算法新聞相似度計(jì)算存在數(shù)據(jù)稀疏問題,本文著重研究了國內(nèi)外文本相似度的計(jì)算方法,提出了適合新聞特點(diǎn)的混合相似度計(jì)算方法。改進(jìn)的相似度計(jì)算方法是在現(xiàn)有的相似度計(jì)算方法的基礎(chǔ)上,考慮了新聞文本中不同詞性的詞語重要性不同、新聞標(biāo)題中的詞語重要高于新聞?wù)闹械脑~語這兩個(gè)特點(diǎn),并融合了基于用戶行為的相似度計(jì)算方式,最后將改進(jìn)的新聞相似度計(jì)算方式用于新聞推薦算法中。針對(duì)協(xié)同過濾算法沒有考慮用戶興趣變化的問題,本文著重研究了國內(nèi)外現(xiàn)有個(gè)性化新聞推薦算法,提出了適應(yīng)用戶興趣變化的個(gè)性化新聞推薦算法。一般來說,用戶近期瀏覽的新聞對(duì)用戶的興趣模型貢獻(xiàn)較大,但用戶興趣具有反復(fù)性的特點(diǎn),即早期的興趣也有可能對(duì)用戶有影響。因此,在協(xié)同過濾算法的基礎(chǔ)上,建立了用戶的近期興趣模型和基于行為反復(fù)的興趣模型,融合得到用戶穩(wěn)定的興趣模型,并用于推薦算法中。論文中的數(shù)據(jù)集采用的是DataCastle的財(cái)新網(wǎng)閱讀記錄,評(píng)測(cè)指標(biāo)是F-measure值和平均絕對(duì)誤差值。適合新聞特點(diǎn)的混合相似度計(jì)算方法與現(xiàn)有的相似度計(jì)算方法都用于推薦算法進(jìn)行對(duì)比,推薦結(jié)果顯示,改進(jìn)后的相似度計(jì)算方法的推薦結(jié)果的Fmeasure值比其他的算法最大高出10.5%,這說明了改進(jìn)后的算法能更精確地計(jì)算新聞相似度值,有效避免了數(shù)據(jù)稀疏問題;適應(yīng)用戶興趣變化的個(gè)性化新聞推薦算法的F-measure值與傳統(tǒng)的協(xié)同過濾算法、現(xiàn)有的推薦算法最大高出11.5%,平均絕對(duì)誤差值最高下降了8%,這說明了改進(jìn)后的算法能更好地反映用戶的興趣。論文最后完成了個(gè)性化新聞推薦系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)。通過對(duì)個(gè)性化新聞推薦系統(tǒng)進(jìn)行總體分析和需求設(shè)計(jì),并將改進(jìn)的推薦算法應(yīng)用于系統(tǒng)設(shè)計(jì)中,最終完成了整個(gè)新聞推薦系統(tǒng)。
[Abstract]:The personalized news recommendation system is based on the historical behavior of each user who has logged into the recommendation system and uses the recommendation algorithm to recommend the news of interest to each user. The personalized news recommendation algorithm based on collaborative filtering algorithm calculates the similarity of news according to the user's historical behavior and completes the recommendation of similar news. The similarity calculation method does not mine the characteristics of news itself and has the problem of sparse data. At the same time, the collaborative filtering algorithm does not consider the dynamic change of user's interest over time. In order to solve the problem of data sparsity in news similarity calculation of recommendation algorithm, this paper focuses on the calculation methods of text similarity at home and abroad, and puts forward a hybrid similarity calculation method suitable for the characteristics of news. The improved similarity calculation method is based on the existing similarity calculation methods, considering the two characteristics of different parts of speech words in news texts, the importance of words in news headlines is higher than the words in news text. Finally, the improved news similarity calculation method is used in news recommendation algorithm. Aiming at the problem that the collaborative filtering algorithm does not consider the change of user interest, this paper focuses on the existing personalized news recommendation algorithm at home and abroad, and proposes a personalized news recommendation algorithm to adapt to the change of user interest. Generally speaking, the news that the user browses recently contributes a lot to the user's interest model, but the user's interest has the characteristic of repetition, that is, the early interest may also have the influence on the user. Therefore, based on the collaborative filtering algorithm, the user's short-term interest model and the behavioral repeated interest model are established, and the user's stable interest model is fused and used in the recommendation algorithm. The data set in this paper uses the data Castle's Caixin net reading record, and the evaluation index is F-measure value and average absolute error value. The hybrid similarity calculation method, which is suitable for news features, is compared with the existing similarity calculation methods, and the recommended results are shown. The recommended Fmeasure value of the improved similarity calculation method is 10.5% higher than that of other algorithms, which shows that the improved algorithm can calculate the news similarity value more accurately and effectively avoid the problem of data sparsity. The F-measure value of personalized news recommendation algorithm which adapts to the change of user's interest and the traditional collaborative filtering algorithm, The existing recommendation algorithm is 11.5 higher than the maximum, and the average absolute error decreases by 8%, which shows that the improved algorithm can better reflect the interest of the user. Finally, the design and implementation of personalized news recommendation system are completed. Through the overall analysis and requirement design of the personalized news recommendation system, the improved recommendation algorithm is applied to the system design, and the whole news recommendation system is finally completed.
【學(xué)位授予單位】：重慶理工大學(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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