A News Recommendation Algorithm Based on SVD and Improved K-means

Sun, Chang; Sun, Gang; Ding, Zhengqi; Liu, Qihang; Ma, Zhiyuan

doi:10.1109/netcit54147.2021.00033

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the limitations inherent in CBF and CF, researchers explored hybrid models. Sun, Chang et al [27] presented hybrid news recommendation algorithms that combine content-based methods (using TF-IDF and K-means clustering) with SVD-based collaborative filtering to improve overall performance. Further, Patoulia, Agori Argyro et al [28] conducted a comparative study of collaborative filtering in product recommendation, which demonstrated that the LightFM library outperforms the surprise library in handling foodservice transactional data, emphasizing LightFM's ability to deal with sparse data and establish personalized recommendations.…”

Section: Traditional-based Recommendation Systemsmentioning

confidence: 99%

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

Siet,

Peng,

Ilkhomjon

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.

show abstract

Section: Traditional-based Recommendation Systemsmentioning

confidence: 99%

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

Siet,

Peng,

Ilkhomjon

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, (Sun, C et al, 2021) propose a method based on SVD and K-means. It utilizes CF method to explore users' latent preferences.…”

Section: Preference-based News Recommendationmentioning

confidence: 99%

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

He,

Wang

2024

International Journal of Distributed Systems and Technologies

View full text Add to dashboard Cite

Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.

show abstract

A News Recommendation Algorithm Based on SVD and Improved K-means

Cited by 2 publications

References 2 publications

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Contact Info

Product

Resources

About