Imputing Trust Network Information in NMF‐based Recommendation Systems

With the emergence of E‐commerce, recommendation system becomes a significant tool which can help both sellers and buyers. It helps sellers by increasing the profits and advertising items to customers. In addition, recommendation systems facilitate buyers to find items they are looking for easily. In recommendation systems, the rating matrix R represents users\u27 ratings for items. The rows in the rating matrix represent the users and the columns represent items. If particular user rates a particular item, then the value of the intersection of the user row and item column holds the rating value. The trust matrix T describes the trust relationship between users. The rows hold the users who create a trust relationship ‐ trustor ‐ and the columns represent users who have been trusted by trustors ‐ trustee ‐

The Reasearch and Design of E-commerce Recommendation System Based on Product Taxonomy

随着Internet迅速发展，电子商务网站越来越受到重视。作为企业对外经营的场所，电子商务系统在为用户提供越来越多的选择的同时，结构也变得越来越复杂。用户在面对大量的商品空间时，很难快速有效的找到自己喜爱的商品。电子商务推荐系统直接与用户进行交互，模拟商店销售人员向用户提供商品推荐，从而帮助用户完成商品购买。电子商务推荐系统在有效提高网站产品的吸引力、转化网站的浏览者为购买者、建立和增强顾客的忠诚度、增加网站的交叉销售量方面起着重要的作用。协作过滤技术是电子商务推荐系统中应用最为成功的个性化推荐技术之一，但是，当系统中的用户数目和商品的数目达到很大规模时，协作过滤技术就将面临很大的挑战。为了解...With the rapid development of Internet, e-commerce website becomes more and more popular. The structure of the e-commerce website is getting complex than ever if aiming to provide growing various products to customers.. This situation makes it hard for customers to find the products they really prefer. Recommendation systems interact directly with customers, and recommend products to customers lik...学位：工学硕士院系专业：计算机与信息工程学院自动化系_系统工程学号：20023104

E-commerce Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns

In E-commerce recommendation systems, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of purchase history data will improve the accuracy of recommendations and mitigate limitations of using only explicit user ratings for recommendations. Existing E-commerce recommendation systems which have combined CF with some form of sequences from purchase history are those referred to as LiuRec09, ChioRec12, and HPCRec18. ChoiRec12 system, HOPE first derives implicit ratings from purchase frequency of users in transaction data which it uses to create user item rating matrix input to CF. Then, it computes the CFPP, the CF-based predicted preference of each target user_u on an item_i as its output from the CF process. Similarly, it derives sequential patterns from the historical purchase database from which it obtains the second output matrix of SPAPP, sequential pattern analysis predicted preference of each user for each item. The final predicted preference of each user for each item FPP is obtained by integrating these two matrices by giving 90\% to SPAPP and 10\% to CFPP so it can recommend items with highest ratings to users. A limitation of HOPE system is that in user item matrix of CF, it does not distinguish between purchase frequency and ratings used for CF. Also in SPM, it recommends items, regardless of whether user has purchased that item before or not. This thesis proposes an E-commerce recommendation system, SEERs (Stacking Ensemble E-commerce Recommendation system), which improves on HOPE system to make better recommendations in the following two ways: i) Learning the best minimum support for SPA, best k similar users for CF and the best weights for integrating the four used matrices. ii) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF not purchased, SPM purchased, SPM not purchased and purchase history matrix which are obtained and merged with the better-learned parameters from (i) above. Experimental results show that by using best weights discovered in training phase, and also separating purchased and not purchased items in CF and sequential pattern mining methods, SEERS provides better precision, recall, F1 score, and accuracy compared to tested systems

Research on Application of Collaborative Filtering Algorithm in the Mobile E-commerce Recommendation System

协同过滤算法是电子商务推荐系统中应用最为成功的推荐算法，很多电子商务企业都采用协同过滤算法作为企业推荐系统的核心算法。推荐技术可以有效解决信息过载问题，增加网站的粘度和交叉销售能力。随着移动互联网的发展，移动电子商务也在逐步兴起，并逐渐占据了大量的市场份额，推荐系统也是必不可少的。与传统电子商务相比，移动电子商务具有移动性，可接受性，安全性，定位性等特点。因此将协同过滤算法应用到移动电子商务推荐系统中时，必须做相应的改进才能适用移动电子商务的特殊环境。 本文对电子商务个性化推荐系统进行了深入研究，认真分析了协同过滤推荐算法本身存在的数据稀疏性问题和冷启动问题。通过SlopeOne算法降低了数据的稀疏性，提高了推荐系统的执行效率，使推荐系统具有较好的推荐质量。冷启动问题可以通过用户的注册信息进行特征分类，根据不同的用户特征对用户进行推荐。 针对移动电子商务领域的特殊性，本文在协同过滤算法中引入了定位技术和时间信息。通过定位技术，移动电子商务在用户的交互过程中能达到很高的个性化程度，从而满足用户对服务的差异化需求。遗忘函数的应用则进一步提高了用户兴趣迁移中的预测精度，可以推荐给用户当前最感兴趣的项目。本文最后通过实验验证了这种改进思路的可行性，证明这种改进算法的确更适合在移动电子商务平台上运用。Collaborative filtering is the most successful e-commerce recommendation algorithm, many e-commerce companies are using collaborative filtering as the core algorithm of the enterprise recommendation system. Recommendation technology can effectively solve the problem of information overload and increase the viscosity of the website and the ability to cross-sell. With the development of mobile Internet, mobile e-commerce is gradually rising and gradually occupies a significant market share, so recommendation system is also essential. Compared with the traditional e-commerce, mobile e-commerce has the mobility, acceptability, safety, location and other characteristics. It does the corresponding improvement in order to apply the special environment of the mobile e-commerce. The dissertation has in-depth study of e-commerce recommendation system and analyzes the data sparsity and cold start problems of the collaborative filtering algorithm carefully. The Slope One algorithm reduces the data sparsity and improves the efficiency and quality of the recommendation system. Cold start problems can be characterized through the user's registration information classification, recommended depending on the user characteristics. Due to the particularity of mobile e-commerce, positioning technology and time information is introduced into the collaborative filtering algorithm. By positioning technology, mobile e-commerce can achieve a high degree of personalization to meet the different demands of the users. Time information applications to further improve the prediction accuracy of the migration of user interest; can recommend to the user the right item. The experiments verify the feasibility of the improvement algorithm, to prove that this improved algorithm is more suitable for mobile e-commerce platform.学位：工程硕士院系专业：软件学院_软件工程学号：2432010115226

Research on Application of Collaborative Filtering Algorithm in the Mobile E-commerce Recommendation System

协同过滤算法是电子商务推荐系统中应用最为成功的推荐算法，很多电子商务企业都采用协同过滤算法作为企业推荐系统的核心算法。推荐技术可以有效解决信息过载问题，增加网站的粘度和交叉销售能力。随着移动互联网的发展，移动电子商务也在逐步兴起，并逐渐占据了大量的市场份额，推荐系统也是必不可少的。与传统电子商务相比，移动电子商务具有移动性，可接受性，安全性，定位性等特点。因此将协同过滤算法应用到移动电子商务推荐系统中时，必须做相应的改进才能适用移动电子商务的特殊环境。 本文对电子商务个性化推荐系统进行了深入研究，认真分析了协同过滤推荐算法本身存在的数据稀疏性问题和冷启动问题。通过SlopeOne算法降低了数...Collaborative filtering is the most successful e-commerce recommendation algorithm, many e-commerce companies are using collaborative filtering as the core algorithm of the enterprise recommendation system. Recommendation technology can effectively solve the problem of information overload and increase the viscosity of the website and the ability to cross-sell. With the development of mobile Inter...学位：工学硕士院系专业：软件学院_计算机软件与理论学号：2432010115226

Method For Detecting Shilling Attacks In E-commerce Systems Using Weighted Temporal Rules

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities

Discovering E-commerce Sequential Data Sets and Sequential Patterns for Recommendation

In E-commerce recommendation system accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that use mining techniques with some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. LiuRec09 system clusters users with similar clickstream sequence data, then uses association rule mining and segmentation based collaborative filtering to select Top-N neighbors from the cluster to which a target user belongs. ChoiRec12 derives a user’s rating for an item as the percentage of the user’s total number of purchases the user’s item purchase constitutes. SuChenRec15 system is based on clickstream sequence similarity using frequency of purchases of items, duration of time spent and clickstream path. HPCRec18 used historical item purchase frequency, consequential bond between clicks and purchases of items to enrich the user-item matrix qualitatively and quantitatively. None of these systems integrates sequential patterns of customer clicks or purchases to capture more complex sequential purchase behavior. This thesis proposes an algorithm called HSPRec (Historical Sequential Pattern Recommendation System), which first generates an E-Commerce sequential database from historical purchase data using another new algorithm SHOD (Sequential Historical Periodic Database Generation). Then, thesis mines frequent sequential purchase patterns before using these mined sequential patterns with consequential bonds between clicks and purchases to (i) improve the user-item matrix quantitatively, (ii) used historical purchase frequencies to further enrich ratings qualitatively. Thirdly, the improved matrix is used as input to collaborative filtering algorithm for better recommendations. Experimental results with mean absolute error, precision and recall show that the proposed sequential pattern mining-based recommendation system, HSPRec provides more accurate recommendations than the tested existing systems

An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM

Recommendation system services have become crucial for users to access personalized goods or services as the global e-commerce market expands. They can increase business sales growth and lower the cost of user information exploration. Recent years have seen a signifi-cant increase in researchers actively using user reviews to solve standard recommender system research issues. Reviews may, however, contain information that does not help consumers de-cide what to buy, such as advertising or fictitious or fake reviews. Using such reviews to offer suggestion services may reduce the effectiveness of those recommendations. In this research, the recommendation in e-commerce is developed using passer learning optimization based on Bi-LSTM to solve that issue (PL optimized Bi-LSTM). Data is first obtained from the product recommendation dataset and pre-processed to remove any values that are missing or incon-sistent. Then, feature extraction is performed using TF-IDF features and features that support graph embedding. Before submitting numerous features with the same dimensions to the Bi-LSTM classifier for analysis, they are integrated using the feature concatenation approach. The Collaborative Bi-LSTM method employs these features to determine if the model is a recommended product. The PL optimization approach, which efficiently adjusts the classifier's parameters and produces an extract output that measures the f1-score, MSE, precision, and recall, is the basis of this research's contributions. As compared to earlier methods, the pro-posed PL-optimized Bi-LSTM achieved values of 88.58%, 1.24%, 92.69%, and 92.69% for dataset 1, 88.46%, 0.48%, 92.43%, and 93.47% for dataset 2, and 92.51%, 1.58%, 91.90%, and 90.76% for dataset 3

Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms

In response to the challenges of learning confusion and information overload in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data scarcity issues of existing traditional recommendation algorithms. Analyze the characteristics of the Knowledge graph of learners and curriculum resources in depth, use the graph Auto encoder to extract the auxiliary information and features in the Knowledge graph and establish the corresponding feature matrix, and use Convolutional neural network for classification and prediction. The experimental results show that this algorithm improves the performance of recommendation systems, improves learners\u27 learning efficiency, and promotes personalized development