scholarly journals User tag weight assessment based on fuzzy theory in mobile social networks

2021 ◽  
Author(s):  
Min Gao ◽  
Li Xu ◽  
Xiaoding Wang ◽  
Xinxin Zhang
Keyword(s):  
Recommendation System ◽  
Fuzzy Theory ◽  
Single Point ◽  
User Profile ◽  
Business Value ◽  
Personalized Recommendation ◽  
User Profiles ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Initial Weight

Abstract Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.

scholarly journals A novel indicator weight tuning method based on fuzzy theory in mobile social networks

2020 ◽  
Author(s):  
Min Gao ◽  
li xu ◽  
Xiaoding Wang ◽  
Xinxin Zhang
Keyword(s):  
Social Networking ◽  
Mobile Communication ◽  
Fuzzy Theory ◽  
Single Point ◽  
Membership Degree ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Initial Weight ◽  
Tuning Method ◽  
Degree Function

Abstract Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

scholarly journals Trajectory Based Location Prediction and Enriched Ontological User Profiles for Efficient Website Recommendation

2020 ◽  
Vol 9 (3) ◽  
pp. 250-257
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
User Profile ◽  
User Profiling ◽  
Location Prediction ◽  
Semantic Features ◽  
User Profiles ◽  
Search Results ◽  
Dynamic Location ◽  
Prediction Approach

The spread over of huge amount of information in the vast area of internet makes difficult for the users to obtain the search items that are relevant to them. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfill their need, it is necessary to know their preferences of search at various contexts. In general, the user profiles are used to determine the taste of the users. The traditional method of user profiling does not provide a complete detail regarding their search. In addition, the search preference of the individuals varies in accordance with time and location. The user profiles do not update the dynamic location changes of the users. The traditional location based recommendation systems suggest the search results based on their location to compensate the dynamic preferences of the users. The drawbacks of the conventional systems are resolved by the Location and User Profile (LUP) based recommendation system. To attain a higher user satisfaction by providing accurate search results, a trajectory based location prediction and enriched ontological user profiles to recommend the appropriate websites to the users is proposed in this paper. In this article, we suggest a novel method for predicting the location of a user's profile using Semantic Trajectory Pattern (STP), based on both the place and semantic features of user trajectories. Our prediction model 's central concept is based on a novel cluster-based prediction approach that evaluates the location of user search data based on the regular activities of related users in the same cluster, calculated by evaluating the typical behavior of users in semantic trajectories. The combination of location information along with enriched ontological user profiles improves the efficiency of the proposed web recommendation system. The experimental results are evaluated using recall, precision and F-measure metrics.

scholarly journals THE RESEARCH TO THE ROBUSTNESS OF RECOMMENDATION SYSTEMS WITH COLLABORATIVE FILTERING TO INFORMATION ATTACKS

2019 ◽  
pp. 95-104
Author(s):  
Yelyzaveta Meleshko ◽  
Vitaliy Khokh ◽  
Oleksandr Ulichev
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
User Profiles ◽  
Injection Attacks ◽  
User Reputation ◽  
General Method

In this article research to the robustness of recommendation systems with collaborative filtering to information attacks, which are aimed at raising or lowering the ratings of target objects in a system. The vulnerabilities of collaborative filtering methods to information attacks, as well as the main types of attacks on recommendation systems - profile-injection attacks are explored. Ways to evaluate the robustness of recommendation systems to profile-injection attacks using metrics such as rating deviation from mean agreement and hit ratio are researched. The general method of testing the robustness of recommendation systems is described. The classification of collaborative filtration methods and comparisons of their robustness to information attacks are presented. Collaborative filtering model-based methods have been found to be more robust than memorybased methods, and item-based methods more resistant to attack than user-based methods. Methods of identifying information attacks on recommendation systems based on the classification of user-profiles are explored. Metrics for identify both individual bot profiles in a system and a group of bots are researched. Ways to evaluate the quality of user profile classifiers, including calculating metrics such as precision, recall, negative predictive value, and specificity are described. The method of increasing the robustness of recommendation systems by entering the user reputation parameter as well as methods for obtaining the numerical value of the user reputation parameter is considered. The results of these researches will in the future be directed to the development of a program model of a recommendation system for testing the robustness of various algorithms for collaborative filtering to known information attacks.

Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

2021 ◽  
pp. 2141013
Author(s):  
N Zafar Ali Khan ◽  
R. Mahalakshmi
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
Personalized Recommendation ◽  
Product Reviews ◽  
Aspect Mining ◽  
Product Recommendation ◽  
Collaborative Recommendation ◽  
Demographic Attributes ◽  
Implicit And Explicit

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.

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%.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

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