本文選題：推薦算法 + 矩陣分解��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：在推薦算法中,基于矩陣分解的協(xié)同過濾算法是使用最為廣泛的推薦技術(shù)之一。本文將對(duì)傳統(tǒng)的矩陣分解算法在共同購買的模式上進(jìn)行擴(kuò)展,基于word2Vec中點(diǎn)際關(guān)系的概念構(gòu)建物品、用戶之間共同購買的關(guān)聯(lián)矩陣,并結(jié)合用戶行為建模和概率矩陣分解,研究共同購買關(guān)系矩陣對(duì)推薦系統(tǒng)的影響。首先,本文根據(jù)物品共同購買的關(guān)系,把物品視作節(jié)點(diǎn),構(gòu)建點(diǎn)際關(guān)系矩陣,同樣的得到用戶點(diǎn)際關(guān)系矩陣。接著本文綜合歷史評(píng)分和共同購買的因素,把這兩個(gè)矩陣和用戶-物品評(píng)分矩陣進(jìn)行分解,得到用戶向量和物品向量。然后本文使用主題模型對(duì)用戶的物品集合和評(píng)論文本進(jìn)行建模得到主題向量,使用這兩個(gè)主題向量來線性擬合用戶向量。最后基于概率矩陣分解算法對(duì)本文模型考慮的因素進(jìn)行建模,并利用最終的用戶和物品向量來預(yù)測(cè)用戶對(duì)物品的購買情況。本文在公開的movielens和amazon數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),結(jié)果表明,基于共同購買和用戶行為的矩陣分解算法在推薦質(zhì)量上對(duì)比PMF、CTR等優(yōu)秀的推薦算法有一定的提升,并且在推薦物品共同購買集上有明顯的提高。
[Abstract]:Among the recommendation algorithms, the collaborative filtering algorithm based on matrix decomposition is one of the most widely used recommendation techniques. In this paper, the traditional matrix factorization algorithm is extended to construct the items based on the concept of point relation in word2Vec, and the association matrix between users to buy together, combined with user behavior modeling and probability matrix decomposition. This paper studies the influence of the common purchase relation matrix on the recommendation system. First of all, according to the relationship of joint purchase of items, this paper regards the items as nodes, constructs the matrix of point relationships, and obtains the same matrix of user points. Then this paper synthesizes the historical score and the common purchase factor, and decomposes the two matrices and the user-item score matrix to obtain the user vector and the item vector. Then, we use the topic model to model the user's item set and comment text to get the topic vector, and use these two theme vectors to fit the user vector linearly. Finally, the factors considered in the model are modeled based on the probabilistic matrix decomposition algorithm, and the final user and item vectors are used to predict the purchase of the items. In this paper, experiments are carried out on the open data sets of movielens and amazon. The results show that the matrix decomposition algorithm based on common purchase and user behavior can improve the quality of recommendation. And in the recommended items on the common purchase set has a significant improvement.
【學(xué)位授予單位】：浙江大學(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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相關(guān)期刊論文 前2條

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本文編號(hào)：1967815