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SVD-based Cross-Domain Recommendation Using K-means Clustering
http://doi.org/10.5626/JOK.2022.49.5.360
Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means Clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a multi-layer neural network, user satisfaction is predicted. Then, items suitable for the user are recommended using matrix factorization, which is a collaborative filtering technique. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase the user satisfaction.
A Weight-based Multi-domain Recommendation System for Alleviating the Cold-Start Problem
http://doi.org/10.5626/JOK.2021.48.10.1090
A recommendation system predicts users’ ratings based on users’ past behaviors and item preferences. One of the most famous types of recommendation systems is the collaborative filtering method that predicts users’ ratings based on the rating information from users with similar preferences. In order to predict the preferences of users, we need adequate information about users’ interactive information on items. Otherwise, it is very difficult to make accurate predictions for users without adequate information. This phenomenon is called the cold-start problem. In this paper, we propose a multi-domain recommendation system that utilizes the rating information of multiple domains. We propose a method that calculates the weight of each auxiliary domain to maximize the confidence of predicted ratings from multiple auxiliary domains and verify the performance of the proposed method through extensive experiments. As a result, we demonstrate that our algorithm produces better recommendation results compared to the classical algorithms simply utilized in multiple domain settings.
Effect of Denoising Autoencoder in the view of Item Popularity Bias
Jinhong Kim, Jae-woong Lee, Jongwuk Lee
http://doi.org/10.5626/JOK.2021.48.5.575
Denoising autoencoder (DAE) is commonly used in recent recommendation systems. It is a type of Autoencoder that trains by giving noise to the input and has shown improved performance compared to autoencoder. In this paper, we analyze the effect of noise in terms of item popularity to interpret the training of DAE. For analysis, we design the experiment in the following two ways. First, we observe the changes of the learned item vector’s L2-norm by giving noise to the autoencoder. Second, by giving noise only to presampled items by popularity, we anlayze whether the improved performance of the DAE is related to item popularity. Results of the experiment showed that the variance of the item vector norm caused by popularity was reduced by noise, and that the accuracy increased when noise was given to the popular items.
Latent Representation Learning for Autoencoder-based Top-K Recommender System
Dongmin Park, Junhyeok Kang, Jae-Gil Lee
http://doi.org/10.5626/JOK.2020.47.2.207
As the number of products on the Internet is growing exponentially, it becomes more difficult for customers to choose the product they want. Many researchers have been actively making efforts to develop appropriate recommender systems that satisfy the potential demand of the customer and increase the profit of the seller. Recently, collaborative filtering methods based on an autoencoder have shown high performance. However, little attention has been paid for improving the recommendation performance by changing the distribution of latent representation. In this paper, we propose the Dense Latent Representation learning method (DenseLR) which is combined with the autoencoder-based collaborative filtering method to further improve product recommendation performance. The key idea of the DenseLR is to tighten collaborative filtering effects on the latent space by effectively densifying the latent representations of user (or item) rating vectors. In performance comparison experiments on three real-world datasets, DenseLR showed the highest recommendation performance for all datasets. Furthermore, DenseLR can be flexibly combined with a wide range of autoencoder-based CF models, and we empirically validated the improvement of the f1@k score ranging from 4.6% to 23.7%.
Analysis of Data Imputation in Recommender Systems
http://doi.org/10.5626/JOK.2017.44.12.1333
Recommender systems (RS) that predict a set of items a target user is likely to prefer have been extensively studied in academia and have been aggressively implemented by many companies such as Google, Netflix, eBay, and Amazon. Data imputation alleviates the data sparsity problem occurring in recommender systems by inferring missing ratings and adding them to the original data. In this paper, we point out the drawbacks of existing approaches and make suggestions for data imputation techniques. We also justify our suggestions through extensive experiments.
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