Recommendation System: A Hybrid Approach for Cold Start and Privacy problem.

2021 ◽  
Vol 8 (5) ◽  
Keyword(s):  
Recommendation System ◽  
Hybrid Approach ◽  
Cold Start ◽  
System A ◽  
Privacy Problem

scholarly journals Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

2021 ◽  
Vol 6 (1) ◽  
pp. 1-16
Author(s):  
Nasy`an Taufiq Al Ghifari ◽  
Benhard Sitohang ◽  
Gusti Ayu Putri Saptawati
Keyword(s):  
Collaborative Filtering ◽  
Bibliometric Analysis ◽  
Recommendation System ◽  
Hybrid Methods ◽  
Hybrid Approach ◽  
Cold Start ◽  
System Research ◽  
Hybrid Techniques ◽  
Social Network Data ◽  
Internet Users

Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However, the study still has some problems left. In the case of cold start new users, actually, the method in the study has handled it with matrix factorizer block and item weight. But it will produce the same results for all users so that the resulting personalization is still lacking. This study aims to map an overview of the themes of recommendation system research that utilizes bibliometric analysis to assess the performance of scientific articles while exposing solution opportunities to cold start problems in the recommendation system. The results of the analysis showed that cold start problems can be solved by utilizing social network data and graph approaches.

scholarly journals METHOD OF FORMING RECOMMENDATIONS USING TEMPORAL CONSTRAINTS IN A SITUATION OF CYCLIC COLD START OF THE RECOMMENDER SYSTEM

2019 ◽  
Vol 4 ◽  
pp. 34-40 ◽  
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi ◽  
Irina Leshchynska
Keyword(s):  
Recommendation System ◽  
Cold Start ◽  
Temporal Constraints ◽  
User Choice ◽  
Filtering Algorithm ◽  
System A ◽  
Source Data ◽  
Collaborative Filtering Algorithm ◽  
Selection Of ◽  
Periodic Changes

The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests.

scholarly journals Product Recommendation System a Comprehensive Review

2021 ◽  
Vol 1022 ◽  
pp. 012021
Author(s):  
Jatin Sharma ◽  
Kartikay Sharma ◽  
Kaustubh Garg ◽  
Avinash Kumar Sharma
Keyword(s):  
Recommendation System ◽  
Comprehensive Review ◽  
Product Recommendation ◽  
System A

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

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