Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

2014 ◽  
Vol 926-930 ◽  
pp. 3004-3007
Author(s):  
Xu Yang Wang ◽  
Heng Liu
Keyword(s):  
Impact Factor ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Recommendation Accuracy ◽  
The Impact ◽  
Rating Information ◽  
Original Rating

The sparsity rating data is one of the main challenges of recommendation system. For this problem, we presented a collaborative filtering recommendation algorithm integrated into co-ratings impact factor. The method reduced the sparsity of rating matrix by filling the original rating matrix. It made the full use of rating information and took the impact on similarity of co-ratings between users into consideration when looking for the nearest neighbor so that the similarities were accurately computed. Experimental results showed that the proposed algorithm, to some extent, improved the recommendation accuracy.

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 Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

2021 ◽  
Vol 11 (20) ◽  
pp. 9554
Author(s):  
Jianjun Ni ◽  
Yu Cai ◽  
Guangyi Tang ◽  
Yingjuan Xie
Keyword(s):  
Collaborative Filtering ◽  
User Characteristics ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Public Data ◽  
Weighted Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

Robust Analysis on a Privacy Preserving Recommendation Algorithm under the KNN Attack

2014 ◽  
Vol 610 ◽  
pp. 717-721 ◽  
Author(s):  
Yan Gao ◽  
Jing Bo Xia ◽  
Jing Jing Ji ◽  
Ling Ma
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Perturbation Techniques ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Robust Analysis ◽  
History Of

— Among algorithms in recommendation system, Collaborative Filtering (CF) is a popular one. However, the CF methods can’t guarantee the safety of the user rating data which cause private preserving issue. In general, there are four kinds of methods to solve private preserving: Perturbation, randomization, swapping and encryption. In this paper, we mimic algorithms which attack the privacy-preserving methods with randomized perturbation techniques. After leaking part of rating history of a customer, we can infer this customer’s other rating history. At the end, we propose an algorithm to enhance the system so as to avoid being attacked.

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 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 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.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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.

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