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.

Research on Goods Recommendation Strategy Based on Decision Tree

2014 ◽  
Vol 687-691 ◽  
pp. 2718-2721
Author(s):  
Jie Gao
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Test Results ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Different Types ◽  
Noise Data ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Strategy

Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users' real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent it sets to get associative sets, and makes recommendations according to users' real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional. Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities are very rich, personal demands are diversification; E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

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.

A Collaborative Filtering Recommendation Algorithm Based on Time Weight

2010 ◽  
Vol 159 ◽  
pp. 667-670
Author(s):  
Yae Dai
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Web Based ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Time Periods ◽  
Filtering Approach

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. Collaborative filtering algorithm is one of the most successful technologies for building personalized recommendation system. But traditional collaborative filtering algorithm does not consider the problem of drifting users interests and the nearest neighbor user set in different time periods, leading to the fact that neighbors may not be the nearest set. In view of this problem, a collaborative filtering recommendation algorithm based on time weight is presented. In the algorithm each rating is assigned a weight gradually decreasing along with time and the weighted rating is used to produce recommendation. The collaborative filtering approach based on time weight not only reduced the data sparsity, but also narrowed the area of the nearest neighbor.

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.

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.

Study on Personalized Recommendation Technology of Digital TV Programs

2013 ◽  
Vol 347-350 ◽  
pp. 3035-3038
Author(s):  
Xiao Bin Wang ◽  
Qing Jun Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Digital Tv ◽  
Personalized Recommendation ◽  
Digital Television ◽  
Content Filtering ◽  
Recommendation Algorithm ◽  
Tv Program ◽  
Collaborative Filtering Algorithm ◽  
Multi Agent

This paper aims at one of key technologies in digital television development ---intelligent personalized recommendation technology of digital TV programs for study. This paper proposes to take advantage of ample TV-Anytime to describe metadata so as to perform specific plans of guide service for TV programs based on TV-Anytime metadata specification. It combines technology such as data mining and artificial intelligence etc with a view of building a personalized TV program recommendation system on the framework of the multi-agent. Besides, a hybrid algorithm with content filtering and collaborative filtering based on the systematical recommendation algorithm has been put forward. In order to overcome the deficiencies of traditional collaborative filtering algorithm which relies on users explicit evaluation, the paper represents an improved algorithm with the footing of content collaborative filtering.

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.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofeng Li ◽  
Dong Li
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Filtering Algorithm ◽  
Collaborative Recommendation ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Community Detection Algorithm

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

scholarly journals Optimization of English Learning Platform Based on a Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jiali Tang
Keyword(s):  
Collaborative Filtering ◽  
Cognitive Ability ◽  
Recommendation System ◽  
Knowledge Map ◽  
English Learning ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Learning Platform ◽  
Depth Analysis ◽  
Collaborative Filtering Algorithm

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.

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