Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity s...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/6/960 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850091504742170624 |
|---|---|
| author | Yayelin Mo Haowen Wang |
| author_facet | Yayelin Mo Haowen Wang |
| author_sort | Yayelin Mo |
| collection | DOAJ |
| description | Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to reflect dataset-specific characteristics. We evaluate our approach using neural models, specifically GRU4REC, and K-Nearest Neighbors (KNN)-based models on music and e-commerce datasets. GRU4REC is selected for its proven sequential model and computational efficiency, serving as a robust baseline against which we compare traditional methods. Our analysis of trend distributions reveals significant variations across datasets, and based on these insights, we propose a hierarchical approach that enhances model performance. Experimental results demonstrate substantial improvements over baseline models, providing a clear pathway for mitigating biases in session-based recommendation systems. |
| format | Article |
| id | doaj-art-cfa71a9250c145b5808cfa26316aba03 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-cfa71a9250c145b5808cfa26316aba032025-08-20T02:42:22ZengMDPI AGMathematics2227-73902025-03-0113696010.3390/math13060960Session-Based Recommendation Method Using Popularity-Stratified Preference ModelingYayelin Mo0Haowen Wang1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100444, ChinaDepartment of Energy Engineering, Zhejiang University, Hangzhou 310027, ChinaLarge-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to reflect dataset-specific characteristics. We evaluate our approach using neural models, specifically GRU4REC, and K-Nearest Neighbors (KNN)-based models on music and e-commerce datasets. GRU4REC is selected for its proven sequential model and computational efficiency, serving as a robust baseline against which we compare traditional methods. Our analysis of trend distributions reveals significant variations across datasets, and based on these insights, we propose a hierarchical approach that enhances model performance. Experimental results demonstrate substantial improvements over baseline models, providing a clear pathway for mitigating biases in session-based recommendation systems.https://www.mdpi.com/2227-7390/13/6/960recommender system biassession-based recommendationpropensity scoringpopularity stratified |
| spellingShingle | Yayelin Mo Haowen Wang Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling Mathematics recommender system bias session-based recommendation propensity scoring popularity stratified |
| title | Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling |
| title_full | Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling |
| title_fullStr | Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling |
| title_full_unstemmed | Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling |
| title_short | Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling |
| title_sort | session based recommendation method using popularity stratified preference modeling |
| topic | recommender system bias session-based recommendation propensity scoring popularity stratified |
| url | https://www.mdpi.com/2227-7390/13/6/960 |
| work_keys_str_mv | AT yayelinmo sessionbasedrecommendationmethodusingpopularitystratifiedpreferencemodeling AT haowenwang sessionbasedrecommendationmethodusingpopularitystratifiedpreferencemodeling |