本文選題：推薦系統(tǒng)　切入點：協(xié)同過濾　出處：《西南石油大學》2017年碩士論文

【摘要】：互聯(lián)網(wǎng)的出現(xiàn)和普及推動了電子商務行業(yè)的崛起,然而,網(wǎng)絡數(shù)據(jù)的急劇膨脹使人們難以獲得有用的信息和服務。推薦系統(tǒng)致力于解決信息過載問題,幫助用戶找到所感興趣的資源,因此已成為當前研究的熱點。協(xié)同過濾是推薦系統(tǒng)的常用核心算法,其基本思想是根據(jù)用戶對產(chǎn)品的評分數(shù)據(jù),找到相似用戶或產(chǎn)品(稱為鄰居),最終借鑒鄰居的偏好進行推薦。然而,已有協(xié)同過濾算法在獲得鄰居時多使用傳統(tǒng)的相似度指標,且很少考慮到用戶與推薦系統(tǒng)之間的交互,影響了預測精度和推薦效果。因此,研究在用戶-推薦系統(tǒng)交互場景中的,利用新穎的相似度指標來進行推薦具有較強的現(xiàn)實意義。本文提出一種基于用戶-推薦系統(tǒng)交互場景的協(xié)同過濾算法,提供用戶感興趣的個性化推薦。第一,提出了近鄰相似度計算指標Triangle,并結合傳統(tǒng)的Jaccard和Cosine相似度,定義了兩種JCT指標JCT_A和JCT_M。Jaccard可以衡量兩個樣本集合的相似性,它被定義為兩個集合的交集和他們的并集元素個數(shù)的比率,其值越高意味著鄰居共同評價過的項目越多,他們的評分相似性也就越可靠;Cosine用兩個評分向量的夾角余弦值來衡量鄰居對不同項目的評分偏好,其值越大,評分向量之間的夾角越小,評分的偏好性越一致;Triangle相似度對評分的絕對數(shù)值敏感,其值越大,評分之間的絕對差距越小。JCT_A將這三種相似度相加,而JCT_M則將它們相乘。第二,設計并實現(xiàn)了批量反饋型的用戶-推薦系統(tǒng)交互場景。用戶隨機登錄推薦系統(tǒng)后,瀏覽推薦列表中所有自己感興趣的項目,并將自己的選擇和評分反饋給系統(tǒng)。系統(tǒng)則根據(jù)用戶的反饋和歷史評分信息,為用戶提供更準確和多樣的推薦。該場景可以高效地為用戶一次推薦多個感興趣的項目,同時又兼顧了信息提供者將自己的資源盡可能多地推薦給用戶的需求,使系統(tǒng)具有一定的挖掘長尾物品的能力。第三,通過兩組在 MovieLens 100K,MovieLens 1M,Each Movie 和 Dou Ban 四個電影評分數(shù)據(jù)集上的實驗驗證算法有效性。第一組實驗以平均絕對誤差(MAE)和均方根誤差(RMSE)為指標,比較PIP、NHSM、JCT__A、JCT_M等七種近鄰計算相似度的評分預測精度。第二組實驗以召回率(recall)、準確率(precision)和覆蓋率(coverage)為指標,首先比較了單一反饋型和批量反饋型的交互場景的TopN推薦效果,然后比較了 Cosine、Pearson、JCTA和JCTM四種近鄰計算相似度在批量反饋型的交互場景的TopN推薦效果。實驗表明,在評分預測方面,在MovieLens 100K,MovieLens 1M和Dou Ban數(shù)據(jù)集上JCT_M的MAE和RMSE值均低于其他相似度指標;在Each Movie數(shù)據(jù)集上,JCT_A取得MAE和RMSE最小值。在TopN推薦方面,在同一種相似度下,批量反饋型的交互場景比單一反饋型的交互場景能得到更高的recall、precision和coverage;JCT_A在Top N推薦上性能優(yōu)于其他相似度。
[Abstract]:The emergence and popularity of the Internet has promoted the rise of e-commerce industry. However, the rapid expansion of network data makes it difficult for people to obtain useful information and services.Recommendation system is dedicated to solve the problem of information overload and help users find interesting resources, so it has become a hot research topic.Collaborative filtering is the core algorithm of recommendation system. The basic idea of collaborative filtering is to find similar users or products (called neighbors or products) according to the users' scoring data.However, the existing collaborative filtering algorithms often use the traditional similarity index in obtaining neighbors, and seldom consider the interaction between users and recommendation systems, which affects the prediction accuracy and recommendation effect.Therefore, it is of great practical significance to study the application of novel similarity index in the user-recommendation system interaction scenario.In this paper, a collaborative filtering algorithm based on user-recommendation system interaction scenario is proposed to provide personalized recommendation of user interest.First, the nearest neighbor similarity index Triangle. combining with the traditional Jaccard and Cosine similarity, two JCT indexes, JCT_A and JCT_M.Jaccard, are defined to measure the similarity between the two sets of samples.It is defined as the ratio of the intersection of two sets to the number of elements of their union, and the higher its value is, the more items neighbors evaluate together,The more reliable their score similarity is, the more reliable Cosine is in measuring neighbors' preferences for different items using the cosine value of the angle between the two score vectors, and the larger the value, the smaller the angle between the score vectors.The more consistent the preference of the score is, the more sensitive the similarity is to the absolute value of the score, and the greater the value, the smaller the absolute difference between the scores. JCTA adds these three similarity degrees and JCT_M multiplies them.Secondly, a batch feedback user-recommendation system interaction scenario is designed and implemented.After the user logs into the recommendation system at random, browse all the items in the recommendation list that are of interest to them, and feedback their selection and rating to the system.The system provides users with more accurate and diverse recommendations based on user feedback and historical scoring information.This scenario can efficiently recommend multiple items of interest to the user at a time, and at the same time, it also takes into account the requirement of the information provider to recommend his own resources to the user as much as possible, so that the system has a certain ability to mine long-tailed items.Thirdly, the validity of the algorithm is verified by two groups of experiments on the four sets of MovieLens 100K movieLens 1Me ach Movie and Dou Ban.In the first group of experiments, the mean absolute error (mae) and the root mean square error (RMSE) were used as the indexes to compare the prediction accuracy of PIPNHSM / JCTS / JCTSP / AJ / JCTSP / AJ / JCTM / AJCTM.In the second group of experiments, the TopN recommendation effect of single feedback interactive scenario and batch feedback interactive scene was compared with the recall rate, accuracy rate and coverage coverage.Then we compare the TopN recommendation effect between Cosine Pearsonian JCTA and JCTM in batch feedback interactive scenarios.The experimental results show that the MAE and RMSE values of JCT_M on MovieLens 100K Movie Lens 1m and Dou Ban datasets are lower than those of other similarity indexes, and the minimum MAE and RMSE values are obtained on Each Movie datasets.In the aspect of TopN recommendation, under the same similarity, the performance of batch feedback interaction scene is higher than that of single feedback interaction scenario, and the performance of covering JCTA is better than that of Top N recommendation.
【學位授予單位】：西南石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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