scholarly journals A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hailong Chen ◽  
Haijiao Sun ◽  
Miao Cheng ◽  
Wuyue Yan
Keyword(s):  
Collaborative Filtering ◽  
Personalized Recommendation ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Weighting Method ◽  
Trust Value ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

scholarly journals Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiushan Zhang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
User Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

scholarly journals A Triangular Personalized Recommendation Algorithm for Improving Diversity

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Cai ◽  
Xiaowang Yang ◽  
Yusheng Huang ◽  
Hongjun Li ◽  
Qiang Sang
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
Online Databases ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Third Dimension ◽  
Triangular Area

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

scholarly journals Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kunni Han
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Data Preprocessing ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

The Improved Collaborative Filtering Recommendation Algorithm Based on Cloud Model

2013 ◽  
Vol 411-414 ◽  
pp. 2292-2296
Author(s):  
Jia Si Gu ◽  
Zheng Liu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cloud Model ◽  
Personalized Recommendation ◽  
Test Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Model Based ◽  
Collaborative Filtering Algorithm

The traditional collaborative filtering algorithm has a better recommendation quality and efficiency, it has been the most widely used in personalized recommendation system. Based on the traditional collaborative filtering algorithm,this paper considers the user interest diversity and combination of cloud model theory.it presents an improved cloud model based on collaborative filtering recommendation algorithm.The test results show that, the algorithm has better recommendation results than other kinds of traditional recommendation algorithm.

Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

2020 ◽  
Vol 34 (10) ◽  
pp. 2059033
Author(s):  
Gang Huang ◽  
Man Yuan ◽  
Chun-Sheng Li ◽  
Yong-he Wei
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Cold Start ◽  
Petroleum Exploration ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

2013 ◽  
Vol 411-414 ◽  
pp. 2223-2228
Author(s):  
Dong Liang Su ◽  
Zhi Ming Cui ◽  
Jian Wu ◽  
Peng Peng Zhao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
User Profile ◽  
Personalized Recommendation ◽  
System Quality ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

scholarly journals A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

2018 ◽  
Vol 10 (12) ◽  
pp. 117 ◽  
Author(s):  
Bo Wang ◽  
Feiyue Ye ◽  
Jialu Xu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Word Embedding ◽  
Purchase Behavior ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Feedback Information ◽  
Recommendation Accuracy

A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals A Personalized Matching System for Management Teaching Resources Based on Collaborative Filtering Algorithm

2020 ◽  
Vol 15 (13) ◽  
pp. 207
Author(s):  
Yongqing Shi ◽  
Xiaojiang Yang
Keyword(s):  
Collaborative Filtering ◽  
Teaching Quality ◽  
Personalized Recommendation ◽  
User Interest ◽  
Teaching Resources ◽  
Collaborative Filtering Algorithm ◽  
User Interest Model ◽  
Set Up ◽  
Learning Interest ◽  
Opening Up

To realize education informatization, it is highly necessary to recommend teaching resources to students that can enhance their learning interest and improve teaching quality. This paper develops a personalized matching system for management teaching resources based on collaborative filtering (CF) algorithm. Firstly, the authors set up a user interest model, designed the flow and algorithm for personalized matching, and improved the similarity calculation method. Next, a personalized recommendation algorithm was developed based on the CF, and a personalized matching engine was constructed with the aid of Apache Mahout. The experimental results show that the proposed CF algorithm can effectively improve the recommendation quality, and push personalized teaching resources to each user; the learners are highly satisfied with the personalized matching system. The research results shed new light on personalized recommendation of teaching resources, opening up a new way to education informatization.

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