Research of Data Sparsity Based on Collaborative Filtering Algorithm

During the traditional collaborative filtering recommendation algorithm be impacted by itself data sparseness problem. It can not provide accurate recommendation result. In this paper, Using traditional collaborative filtering algorithm and the concept of similar level, take advantage of the idea of data populating to solve sparsity problem, then using the Weighted Slope One algorithm to recommend calculating. Experimental results show that the improved algorithm solved the problem of the recommendation results of low accuracy because of the sparse scoring matrix, and it improved the algorithm recommended results to a certain extent.

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Collaborative Filtering Algorithm Based on User Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1044 ◽

2013 ◽

Vol 411-414 ◽

pp. 1044-1048

Author(s):

Zhao Deng ◽

Jin Wang

Keyword(s):

Collaborative Filtering ◽

Nearest Neighbor ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

User Clustering ◽

Similarity Algorithm ◽

Collaborative Filtering Algorithm ◽

Similarity Computation ◽

Improved Algorithm

To overcome the uncertainty of the users neighborhoods in the recommendation algorithm of nearest neighbor, an improved collaborative filtering algorithm based on user clustering is proposed. This improved algorithm filters the users by their features, and the improved cosine similarity algorithm is used for the item similarity computation. Experiments on the MovieLens dataset showed that, compared with Lis collaborative filtering algorithm, the recommendation quality of the improved algorithm is more accurate and the category coverage is larger.

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A Collaborative Filtering Recommendation Algorithm Combined with User and Item

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1878 ◽

2014 ◽

Vol 513-517 ◽

pp. 1878-1881

Author(s):

Feng Ming Liu ◽

Hai Xia Li ◽

Peng Dong

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Experimental Results ◽

Personalized Recommendation ◽

User Evaluation ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

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Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

Scientific Programming ◽

10.1155/2021/7427409 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Lianhuan Li ◽

Zheng Zhang ◽

Shaoda Zhang

Keyword(s):

Collaborative Filtering ◽

Hybrid Algorithm ◽

Cold Start ◽

System Optimization ◽

Data Matrix ◽

Content Filtering ◽

Data Sparsity ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Improved Algorithm

This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.

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A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3865 ◽

2013 ◽

Vol 756-759 ◽

pp. 3865-3868 ◽

Cited By ~ 1

Author(s):

Dan Er Chen ◽

Yu Long Ying

Keyword(s):

Electronic Commerce ◽

Collaborative Filtering ◽

Recommender Systems ◽

Bipartite Graph ◽

Rapid Growth ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Sparsity Problem

With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.

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An Overview of Collaborative Filtering Recommendation Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3899 ◽

2013 ◽

Vol 756-759 ◽

pp. 3899-3903

Author(s):

Ping Sun ◽

Zheng Yu Li ◽

Zi Yang Han ◽

Feng Ying Wang

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Evaluation Method ◽

Decisive Role ◽

Classical Solutions ◽

Critical Problem ◽

Recommendation Algorithm ◽

Data Sparseness ◽

Collaborative Filtering Algorithm ◽

And Performance

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420590338 ◽

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.

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Research of Personalized Course Recommended Algorithm based on the Hybrid Recommendation

MATEC Web of Conferences ◽

10.1051/matecconf/201817303067 ◽

2018 ◽

Vol 173 ◽

pp. 03067

Author(s):

Qing Yang ◽

Peiling Yuan ◽

Xi Zhu

Keyword(s):

Collaborative Filtering ◽

Association Rule ◽

High Performance ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Elective Course ◽

Recommended Algorithm ◽

Hybrid Recommendation

This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provides a academic reference for the design of high performance elective system. The experiment uses the student scores data of a college as the test set and analyzes results and recommended quality of personalized elective course. According to the results of the experimental results, the quality of the improved hybrid recommendation algorithm is better.

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A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

Computational Intelligence and Neuroscience ◽

10.1155/2018/1425365 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Xu Yu ◽

Jun-yu Lin ◽

Feng Jiang ◽

Jun-wei Du ◽

Ji-zhong Han

Keyword(s):

Linear Regression ◽

Collaborative Filtering ◽

Feature Construction ◽

Target Domain ◽

Regression Problem ◽

Filtering Algorithm ◽

Weighted Linear Regression ◽

Cross Domain ◽

Collaborative Filtering Algorithm ◽

Sparsity Problem

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

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Study on Recommendation Method Based on Product Evaluation Concept Tree and Collaborative Filtering Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.519-520.401 ◽

2014 ◽

Vol 519-520 ◽

pp. 401-404

Author(s):

Mei Hao ◽

Bo Qing Zhang

Keyword(s):

Collaborative Filtering ◽

Product Evaluation ◽

Tablet Pc ◽

Experiment Data ◽

Raw Data ◽

Filtering Algorithms ◽

Filtering Algorithm ◽

Data Sparseness ◽

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

International Journal of Modern Physics C ◽

10.1142/s0129183110015786 ◽

2010 ◽

Vol 21 (10) ◽

pp. 1217-1227 ◽

Cited By ~ 24

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.

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