scholarly journals Hybrid Measuring the Similarity Value Based on Genetic Algorithm for Improving Prediction in A Collaborative Filtering Recommendation System.

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Muaadh Abdo Mohammed Ahmed AL sabri
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Forecast Accuracy ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Personalized Recommendation ◽  
Education Economics ◽  
Standard Methods ◽  
Wide Range ◽  
Suitable Product

In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity.  However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.

Enhancing Collaborative Filtering Recommendation by Utilizing Improved Ant Colony Optimization Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3793-3799
Author(s):  
Zhong Liang Li ◽  
Chen Xiao Hu ◽  
Xu Yang Wei ◽  
Teng Fei Zou ◽  
Hao Ran Zhang ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Ant Colony Algorithm ◽  
Recommendation System ◽  
Ant Colony ◽  
Personalized Recommendation ◽  
Access Time ◽  
Ant Colony Optimization Algorithm ◽  
Access Frequency ◽  
Collaborative Filtering Algorithm ◽  
Improved Ant Colony Algorithm

Collaborative filtering (CF) is the most widely used and successful personalized recommendation technology in web usage mining. The traditional collaborative filtering algorithm based on user static evaluation of the item's neighbour to predict changes of the users’ interests, however, the user’s interest will make a difference over time. Taking the dynamic changes the user’s interest into account in the process, this paper presents a dynamic collaborative filtering recommendation method based on improved ant colony algorithm (EACF). Improved ant colony algorithm takes into account the user access time and access frequency, which can be more representative of the true interests of users. When generating the recommendation, this method not only takes into account the item’s score, but also will take into account intensity of “interest pheromone” on each item. Experimental results show that the EACF can significantly improve the prediction accuracy of the recommendation system compared with traditional CF.

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.

The Development of Building Materials Recommendation System Based on Collaborative Filtering

2013 ◽  
Vol 281 ◽  
pp. 597-602 ◽  
Author(s):  
Guo Fang Kuang ◽  
Chun Lin Kuang
Keyword(s):  
Collaborative Filtering ◽  
Building Materials ◽  
Recommendation System ◽  
High Strength ◽  
Data Sets ◽  
User Interest ◽  
Insulation Materials ◽  
Target User ◽  
Wide Range ◽  
Materials Used

The building materials used in building materials collectively referred to as building materials. New building materials, including a wide range of insulation materials, insulation materials, high strength materials, breathing material belong to the new material. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of building materials recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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.

The Design and Implementation of E-Commerce Personalized Services Based on Collaborative Filtering Recommendation System

2014 ◽  
Vol 687-691 ◽  
pp. 2039-2042 ◽  
Author(s):  
Meng Han
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Reference Value ◽  
Personalized Recommendation ◽  
Site Management ◽  
Logical Model ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Personalized Services

In this paper, in accordance with the need of e-commerce site management, constructing the logical model of the personalized recommendation system, and use filtering recommendation algorithm to design the personalized recommendation engine. It is necessary to provide certain reference value to improve the personalized recommendation efficiency of e-commerce sites.

scholarly journals 5G Multimedia Precision Marketing Based on the Improved Multisensor Node Collaborative Filtering Recommendation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Si ◽  
Min Zhou ◽  
Yingfang Qiao
Keyword(s):  
Collaborative Filtering ◽  
Social Relations ◽  
Recommendation System ◽  
Rapid Development ◽  
Auxiliary Information ◽  
Web Technology ◽  
Practical Significance ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Regularization Term

The rapid development of web technology has brought new problems and challenges to the recommendation system: on the one hand, the traditional collaborative filtering recommendation algorithm has been difficult to meet the personalized recommendation needs of users; on the other hand, the massive data brought by web technology provides more useful information for recommendation algorithms. How to extract features from this information, alleviate sparsity and dynamic timeliness, and effectively improve recommendation quality is a hot issue in the research of recommendation system algorithms. In view of the lack of an effective multisource information fusion mechanism in the existing research, an improved 5G multimedia precision marketing based on an improved multisensor node collaborative filtering recommendation algorithm is proposed. By expanding the input vector field, the features of users’ social relations and comment information are extracted and fused, and the problem of collaborative modelling of these two kinds of important auxiliary information is solved. The objective function is improved, the social regularization term and the internal regularization term in the vector domain are analysed and added from the perspective of practical significance and vector structure, which alleviates the overfitting problem. Experiments on a large number of real datasets show that the proposed method has higher recommendation quality than the classical and mainstream baseline algorithm.

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