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.

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.

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.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

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.

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.

Research on Personalized Recommendation Technology for Tourism Industry - A Perspective of a System Framework Design

2011 ◽  
Vol 219-220 ◽  
pp. 1276-1280 ◽  
Author(s):  
Mu Zhang ◽  
Jie Miao ◽  
Jing Luo ◽  
Jian Hua Lan
Keyword(s):  
Recommender System ◽  
Information Acquisition ◽  
Tourism Industry ◽  
Personalized Recommendation ◽  
Travel Destination ◽  
Different Types ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System ◽  
Recommender Algorithm ◽  
Hybrid Recommender

When massive information brings people more channels to attain messages, there generates different types of new personalized recommendation systems. As the important sector for social development, tourism industry suffers the problem of information over-loading. In the construction of personalized recommender system mainly involves two problems: information acquisition and personalized recommender. This recommender system is able to form client’s databank after user’s login and assessment for various travel destination and products to support more accurate user’s information mining. The authors adopt improved hybrid recommender algorithm through combining collaborative filtering algorithm with content-based recommendation algorithm. The relationship of user and travel destination can be classified as the interested and disinterested. So it can predict the degree of new users’ enthusiasm towards different types of travel destination to realize personalized travel recommendation.

Collaborative Filtering Algorithm Based on User Clustering

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.

scholarly journals THE RECOMMENDATION ALGORITHM FOR AN ONLINE ART GALLERY

2018 ◽  
Vol 7 (2) ◽  
pp. 108-119
Author(s):  
Waldemar Karwowski ◽  
Marian Rusek ◽  
Joanna Sosnowska
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation Systems ◽  
User Profile ◽  
Art Gallery ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Filtering Technique

The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.

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