Topic recommendation system using personalized fuzzy logic interest set

2020 ◽  
pp. 1-11
Author(s):  
Wenqiang Zhu
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Experimental Tests ◽  
User Interest ◽  
Fuzzy Logic Systems ◽  
Fine Grained ◽  
User Interests ◽  
Collaborative Recommendation

First, the recommendation system and its advantages are introduced in detail, and based on the characteristics of the intelligent topic logical interest set resource and user behavior in the existing intelligent topic logical interest set resource platform, a personalized fuzzy logic model of the intelligent topic logical interest set resource is established and adapted to it. The personalized fuzzy logic user personalized fuzzy logic interest model of personalized fuzzy logic is designed, and the user personalized fuzzy logic interest transfer method is designed to simulate the user learning process. Secondly, on the basis of the established model, according to the idea of collaborative filtering, the personalized fuzzy logic user’s personalized fuzzy logic interest value and the user’s rating of resources are respectively predicted, and the two prediction results are combined to recommend resources to the user. Finally, the ontology is applied to user interest description, and a method based on personalized fuzzy logic user rough interest vector and nearest neighbor concept aggregation is proposed to find fine-grained user interest and recommend interest resources. Experimental tests show that this method can better describe the composition and development of user interests, making the recommendation effect of interest resources for specific users more accurate and reliable. The problem of collaborative recommendation in personalized fuzzy logic systems is further studied, the basic principles and typical technologies of collaborative recommendation are analyzed, and the collaborative recommendation method based on users with similar interests and the collaborative recommendation method based on weighted association rules are proposed.

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.

scholarly journals An Exploratory Study of Hierarchical Fuzzy Systems Approach in A Recommendation System

2019 ◽  
Vol 14 (2) ◽  
pp. 174-186
Author(s):  
Tajul Rosli Razak ◽  
Iman Hazwam Abd Halim ◽  
Muhammad Nabil Fikri Jamaludin ◽  
Mohammad Hafiz Ismail ◽  
Shukor Sanim Mohd Fauzi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Systems ◽  
Recommendation System ◽  
Systems Approach ◽  
Fuzzy Logic Systems ◽  
Input Variables ◽  
Hierarchical Fuzzy System ◽  
Hierarchical Fuzzy Systems ◽  
Insight Into ◽  
Logic System

Recommendation system, also known as a recommender system, is a tool to help the user in providing asuggestion of a specific dilemma. Recently, the interest in developing a recommendation system in manyfields has increased. Fuzzy Logic system (FLSs) is one of the approaches that can be used to model therecommendation systems as it can deal with uncertainty and imprecise information. However, one of thefundamental issues in FLS is the problem of the curse of dimensionality. That is, the number of rules inFLSs is increasing exponentially with the number of input variables. One effective way to overcome thisproblem is by using Hierarchical Fuzzy System (HFSs). This paper aims to explore the use of HFSs forRecommendation system. Specifically, we are interested in exploring and comparing the HFS and FLS forthe Career path recommendation system (CPRS) based on four key criteria, namely topology, the numberof rules, the rules structures and interpretability. The findings suggested that the HFS has advantagesover FLS towards improving the interpretability models, in the context of a recommendation systemexample. This study contributes to providing an insight into the development of interpretable HFSs in theRecommendation systems. Keywords: Fuzzy Logic Systems, Hierarchical Fuzzy Systems, Recommendation Systems

scholarly journals Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

2021 ◽  
Author(s):  
Xiangyuan Li ◽  
Fei Ding ◽  
Suju Ren ◽  
Jianmin Bao ◽  
Ruoyu Su ◽  
...  
Keyword(s):  
High Speed ◽  
Recommendation System ◽  
Short Term Memory ◽  
User Behavior ◽  
Data Sets ◽  
Mixed Traffic ◽  
User Interest ◽  
Travel Information ◽  
Lstm Network ◽  
Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

scholarly journals Deep Interest Evolution Network for Click-Through Rate Prediction

2019 ◽  
Vol 33 ◽  
pp. 5941-5948 ◽  
Author(s):  
Guorui Zhou ◽  
Na Mou ◽  
Ying Fan ◽  
Qi Pi ◽  
Weijie Bian ◽  
...  
Keyword(s):  
User Behavior ◽  
Target Item ◽  
State Of The Art ◽  
User Interest ◽  
User Interests ◽  
Sequential Structure ◽  
Changing Trend ◽  
Click Through Rate ◽  
Novel Model ◽  
Deep Interest

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user clicking on the item, has become one of the core tasks in the advertising system. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, little work considers the changing trend of the interest. In this paper, we propose a novel model, named Deep Interest Evolution Network (DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7% improvement on CTR.

scholarly journals An English Teaching Resource Recommendation System Based on Network Behavior Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommendation System ◽  
User Behavior ◽  
Teaching Resources ◽  
English Teaching ◽  
Collaborative Recommendation ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Local User

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

scholarly journals Streamlined News Recommendation System Using a Variable Markov Model

2021 ◽  
Author(s):  
Dejan Spanovic
Keyword(s):  
Markov Model ◽  
Recommendation System ◽  
News Article ◽  
User Interest ◽  
Single Model ◽  
User Interests ◽  
Single Solution ◽  
News Recommendation ◽  
And Control ◽  
To Receive

Providing news to users in a news article recommendation system is a balancing act between delivering news that is recent and news that is relevant to their interests. Users should be able to receive a stream of similar articles that interest them and control their traversal through the topics of news articles in a stream-wise fashion as well. A Variable Markov Model (VMM), built on trends in recently published news articles, is proposed as a single solution to categorically cater news to all users with minimal overhead and maintenance. This single model provided to all users throughout experimentation has shown that, though it is not built based on user interests, it is applicable as a basis for applying user interest and trend factors upon to achieve catered and novel news recommendation experiences.

scholarly journals Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Qiaoqiao Tan ◽  
Fang’ai Liu
Keyword(s):  
User Behavior ◽  
User Interest ◽  
Short Term ◽  
User Interests ◽  
User Behaviors ◽  
Current State ◽  
Proposed Model ◽  
Dependence Problem ◽  
Behavior Sequences

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations. First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature. Finally, combined with long-term and short-term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.

scholarly journals Streamlined News Recommendation System Using a Variable Markov Model

2021 ◽  
Author(s):  
Dejan Spanovic
Keyword(s):  
Markov Model ◽  
Recommendation System ◽  
News Article ◽  
User Interest ◽  
Single Model ◽  
User Interests ◽  
Single Solution ◽  
News Recommendation ◽  
And Control ◽  
To Receive

Providing news to users in a news article recommendation system is a balancing act between delivering news that is recent and news that is relevant to their interests. Users should be able to receive a stream of similar articles that interest them and control their traversal through the topics of news articles in a stream-wise fashion as well. A Variable Markov Model (VMM), built on trends in recently published news articles, is proposed as a single solution to categorically cater news to all users with minimal overhead and maintenance. This single model provided to all users throughout experimentation has shown that, though it is not built based on user interests, it is applicable as a basis for applying user interest and trend factors upon to achieve catered and novel news recommendation experiences.

Research on Web Application Technology in Distance Education Personalized Recommendation System

2013 ◽  
Vol 859 ◽  
pp. 416-421 ◽  
Author(s):  
Zhu Feng Qiao ◽  
Jian Xin Guo ◽  
Ji Chun Zhao
Keyword(s):  
Distance Education ◽  
Web Application ◽  
Recommendation System ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
User Interest ◽  
Application Technology ◽  
Recommendation Algorithm ◽  
Teaching Resources ◽  
Collaborative Recommendation

With the rapid development of distance education, distance educations teaching resources has a number of large, but, it is difficult to find suitable courseware resources in time. The system constructs and improves the user interest model of the vector space, through the analysis of user behavior. The system uses the content-based recommendation algorithm, user-base collaborative recommendation and item-base collaborative recommendation algorithm to implement distance education resource recommender system. So as to provide distance education personalized recommendation web application technology as far as possible to meet the user needs, enhance the user experience of distance education system.

Sign in / Sign up

Export Citation Format

Share Document