A Sparse Image Recommendation Model Using Content and User Preference Information

2016 ◽  
Author(s):  
Lei Liu
Keyword(s):  
User Preference ◽  
Preference Information ◽  
Image Recommendation

Study on Collaborative Filtering Recommendation Model Fusing User Reviews

2019 ◽  
Vol 23 (5) ◽  
pp. 864-873
Author(s):  
Heyong Wang ◽  
◽  
Ming Hong ◽  
Jinjiong Lan
Keyword(s):  
Collaborative Filtering ◽  
Topic Model ◽  
User Preference ◽  
High Dimensional ◽  
Fusion Method ◽  
User Reviews ◽  
Preference Information ◽  
Collaborative Recommendation ◽  
Public Datasets ◽  
Rating Information

The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

scholarly journals Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 771
Author(s):  
Aleix Alcacer ◽  
Irene Epifanio ◽  
Jorge Valero ◽  
Alfredo Ballester
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
Simulation Study ◽  
Information Sources ◽  
Low Cost ◽  
Anthropometric Measurements ◽  
User Preference ◽  
Size Mismatch ◽  
Preference Information ◽  
First Time

Size mismatch is a serious problem in online footwear purchase because size mismatch implies an almost sure return. Not only foot measurements are important in selecting a size, but also user preference. This is the reason we propose several methodologies that combine the information provided by a classifier with anthropometric measurements and user preference information through user-based collaborative filtering. As novelties: (1) the information sources are 3D foot measurements from a low-cost 3D foot digitizer, past purchases and self-reported size; (2) we propose to use an ordinal classifier after imputing missing data with different options based on the use of collaborative filtering; (3) we also propose an ensemble of ordinal classification and collaborative filtering results; and (4) several methodologies based on clustering and archetype analysis are introduced as user-based collaborative filtering for the first time. The hybrid methodologies were tested in a simulation study, and they were also applied to a dataset of Spanish footwear users. The results show that combining the information from both sources predicts the foot size better and the new proposals provide better accuracy than the classic alternatives considered.

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Sign in / Sign up

Export Citation Format

Share Document