scholarly journals Hybrid Recommendation Scheme Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fangpeng Ming ◽  
Liang Tan ◽  
Xiaofan Cheng
Keyword(s):  
Deep Learning ◽  
Massive Data ◽  
Nonlinear Interactions ◽  
Recommendation Algorithm ◽  
Long Tail ◽  
Test Dataset ◽  
Learning Techniques ◽  
Recommendation Algorithms ◽  
Cn Algorithm ◽  
Hybrid Recommendation

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.

scholarly journals Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

2021 ◽  
Author(s):  
Zhisheng Yang ◽  
Jinyong Cheng
Keyword(s):  
Deep Learning ◽  
Auxiliary Information ◽  
Substantial Improvement ◽  
User Preference ◽  
Knowledge Graph ◽  
Historical Records ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Recommendation Algorithms

Abstract In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.

scholarly journals INVESTMENT DECISION OF TOURISM LEISURE PROJECT BASED ON COLLABORATIVE FILTERING ALGORITHM

2018 ◽  
Vol 48 (4) ◽  
pp. 293-297
Author(s):  
B. X. XUE ◽  
T. LIU
Keyword(s):  
Collaborative Filtering ◽  
Investment Decision ◽  
Big Bang ◽  
Recommendation Algorithm ◽  
Long Tail ◽  
Filtering Algorithm ◽  
Present Information ◽  
Collaborative Filtering Algorithm ◽  
The Big Bang ◽  
Hybrid Recommendation

In the era of the Big Bang, users have to spend a lot of time looking for the information they really need, and the search engine can't present information that is not described by the user. Based on the User based collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm, this paper proposes User-CF algorithm, User-CF-1 algorithm, Item-CF algorithm, Item-CF-1 algorithm, and finally integrates four algorithms to obtain the cooperative strategy based on collaborative filtering is a hybrid recommendation algorithm, namely the Final algorithm. It has been verified that the Final algorithm can effectively solve the long tail problem in the travel strategy recommendation and greatly increase the coverage of the recommended strategy.

scholarly journals Predicting the Kijang Emas Bullion Price using LSTM Networks

2020 ◽  
Vol 8 (2) ◽  
pp. 11-18
Author(s):  
Mohammad Hafiz Ismail ◽  
Tajul Rosli Razak
Keyword(s):  
Deep Learning ◽  
The Other ◽  
Test Dataset ◽  
Long Period ◽  
Price Data ◽  
Learning Techniques ◽  
Future Value ◽  
Bidirectional Lstm

This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.

Hybrid recommendation model based on deep learning and Stacking integration strategy

2020 ◽  
Vol 24 (6) ◽  
pp. 1329-1344
Author(s):  
Xiaolan Xie ◽  
Shantian Pang ◽  
Jili Chen
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Structural Features ◽  
Internet Technology ◽  
Inner Product ◽  
Learning Technology ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Integration Strategy ◽  
Hybrid Recommendation

In the traditional recommendation algorithms, due to the rapid development of deep learning and Internet technology, user-item rating data is becoming increasingly sparse. The simple inner product interaction mode adopted by the collaborative filtering method has a cold start problem and cannot learn the complex nonlinear structural features between users and items, while the content-based algorithm encounters the difficulty of effective feature extraction. In response to this problem, a hybrid model is proposed based on deep learning and Stacking integration strategy. The traditional recommendation algorithm is first fused by using the Stacking integration strategy to make up for the shortcomings of the single recommendation algorithm to achieve better recommendation performance. The fusion-based model learns the more abstract and deeper nonlinear interaction features by deep learning technology, which makes the model performance gain further. The experiment comparison on the MovieLens-1m dataset shows that the proposed hybrid recommendation model can significantly improve the accuracy of rating prediction.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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