本文選題：個性化推薦 + 瀏覽軌跡�。� 參考：《北京化工大學(xué)》2016年碩士論文

【摘要】：隨著電子商務(wù)網(wǎng)站迅速發(fā)展,推薦系統(tǒng)在這些網(wǎng)站中得到了廣泛的應(yīng)用。目前應(yīng)用最廣泛的個性化推薦算法是協(xié)同過濾推薦算法,但是此方法存在稀疏矩陣與冷啟動問題�；谟脩魹g覽記錄來預(yù)測用戶偏愛是緩解這些問題的一個重要研究方向。根據(jù)用戶在電子商務(wù)網(wǎng)站的訪問日志,提取出用戶的瀏覽序列,即用戶瀏覽軌跡,然后以用戶瀏覽軌跡為基礎(chǔ),挖掘用戶偏愛,并建立偏愛模型,為用戶推薦偏愛商品,解決因為缺少歷史購買及評分記錄引起的冷啟動問題。目前通過分析用戶瀏覽軌跡為用戶推薦商品的方法主要從瀏覽軌跡中商品與下一個商品關(guān)系角度出發(fā)考慮,而本課題從瀏覽軌跡中被瀏覽商品與最終被購買商品關(guān)系角度出發(fā),提出兩種推薦方法,一種基于購買轉(zhuǎn)移關(guān)系,另一種基于商品特征趨勢�；谫徺I轉(zhuǎn)移關(guān)系的推薦方法,依據(jù)瀏覽軌跡中商品與最終被購買商品轉(zhuǎn)移關(guān)系,同時考慮用戶瀏覽軌跡與購買記錄的時效性,對過時數(shù)據(jù)采用衰減策略,另外考慮瀏覽軌跡中商品的順序,對軌跡中瀏覽距離不同轉(zhuǎn)移關(guān)系應(yīng)用不同的權(quán)重,構(gòu)建購買轉(zhuǎn)移概率模型,向用戶推薦商品；基于商品特征趨勢的推薦方法根據(jù)商品特征屬性統(tǒng)計用戶瀏覽軌跡中商品特征趨勢,構(gòu)建Markov特征趨勢模型,當(dāng)新用戶在線瀏覽商品時,根據(jù)Markov特征趨勢模型和用戶當(dāng)前瀏覽軌跡中可變商品特征預(yù)測用戶偏愛商品的特征集,查找最符合這些特征的商品推薦給當(dāng)前用戶,該方法也同時考慮歷史數(shù)據(jù)的時效性和特征集在瀏覽軌跡中的順序性。實驗證明,這兩種方法相對于已有的基于用戶瀏覽路徑的方法有較好改進(jìn),都取得了比較好的推薦效果,提高了推薦算法的準(zhǔn)確度與召回率,在一定程度上解決了新用戶冷啟動問題和新物品冷啟動問題。
[Abstract]:With the rapid development of e-commerce websites, recommendation system has been widely used in these websites.At present, the most widely used personalized recommendation algorithm is collaborative filtering recommendation algorithm, but this method has the problem of sparse matrix and cold start.Predicting user preference based on user browsing records is an important research direction to alleviate these problems.According to the user's visit log in the E-commerce website, the user's browsing sequence is extracted, that is, the user's browsing track, and then based on the user's browsing track, the user's preference is excavated, and the preference model is established to recommend the preferred products for the user.Resolve cold startup problems due to lack of historical purchase and rating records.At present, the method of recommending goods for users by analyzing the user's browsing path is mainly considered from the perspective of the relationship between the goods in the browsing path and the next commodity, while this topic starts from the perspective of the relationship between the goods being browsed and the goods that are finally purchased.Two recommendation methods are proposed, one is based on purchase transfer relationship, the other is based on commodity feature trend.Based on the recommendation method of the purchase transfer relationship, according to the relationship between the goods in the browsing track and the final purchased goods, and considering the timeliness of the user's browsing track and purchase record, the attenuation strategy is adopted for the outdated data.In addition, considering the order of the goods in the browsing path, applying different weights to the different transfer relationships of the browsing distance in the trajectory, the model of purchase transfer probability is constructed, and the goods are recommended to the user.According to the commodity feature attribute, the Markov feature trend model is constructed according to the commodity feature attribute. When the new user browses the product online, it constructs the Markov feature trend model.According to the Markov feature trend model and the variable commodity feature in the current browsing path of the user, the feature set of the user's preference is predicted, and the products most suitable for these features are found to be recommended to the current user.The method also considers the timeliness of historical data and the sequence of feature sets in browsing trajectories.Experimental results show that the two methods are better than the existing methods based on user browsing path, and both have better recommendation effect, and improve the accuracy and recall rate of the recommendation algorithm.To a certain extent, the new user cold start problem and new items cold start problem.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3

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