Update 2010-10-01-paper-title-number-2.md

_publications/2010-10-01-paper-title-number-2.md

@@ -2,23 +2,11 @@
 title: "User Plays a Role: User-insight Multi-modal Recommendation"
 collection: publications
 permalink: /publication/2010-10-01-paper-title-number-2
-excerpt: 'Multi-Modal Recommendation (MMRec) aims to help users explore their potential interested items based on multi-modal information input and has been widely used in e-commerce platforms. Recent works mainly focus on modeling item-side information. However, they ignore the abundant semantic information from the user-information modeling, including age, gender, feedback, etc. Such imbalanced attention to item and user leads to inadequate expressiveness of comprehensive interests. 
-In this paper, we propose a novel \textbf{U}ser-\textbf{i}nsight \textbf{M}ulti-modal recommendation framework, termed UiM.
-This framework improves user modeling in three aspects:
-Firstly, to better explore the primary interests from a large-scale item pool, we propose to construct an enriched user profile to re-distribute attention to users' historical interactions.
-Secondly, to further disentangle compact representations from heterogeneous items, we propose to apply multi-interest feature extraction on re-attentioned item features.
-Moreover, an intrinsic shortage of a trivial recommender system is that it fails to access user feedback for in-place result adjustment. As a solution, we access pseudo feedback beforehand from an intelligent agent, then accordingly perform potential adjustments to recommendation candidates for finer results.
-Extensive experiments show that our model outperforms state-of-the-art multi-modal recommendation models in three public datasets.'
+excerpt: "Multi-Modal Recommendation (MMRec) aims to help users explore their potential interested items based on multi-modal information input and has been widely used in e-commerce platforms. Recent works mainly focus on modeling item-side information. However, they ignore the abundant semantic information from the user-information modeling, including age, gender, feedback, etc. Such imbalanced attention to item and user leads to inadequate expressiveness of comprehensive interests. In this paper, we propose a novel User-insight Multi-modal recommendation framework, termed UiM. This framework improves user modeling in three aspects: Firstly, to better explore the primary interests from a large-scale item pool, we propose to construct an enriched user profile to re-distribute attention to users' historical interactions. Secondly, to further disentangle compact representations from heterogeneous items, we propose to apply multi-interest feature extraction on re-attentioned item features. Moreover, an intrinsic shortage of a trivial recommender system is that it fails to access user feedback for in-place result adjustment. As a solution, we access pseudo feedback beforehand from an intelligent agent, then accordingly perform potential adjustments to recommendation candidates for finer results. Extensive experiments show that our model outperforms state-of-the-art multi-modal recommendation models in three public datasets."
 date: 2010-10-01
-venue: 'Journal 1'
-paperurl: 'https://KevinXu-01.github.io/home/files/paper2.pdf'
-citation: 'Jingyu Xu, Zechao Hu, Hao Li, et al. User Plays a Role: User-insight Multi-modal Recommendation. IEEE Transactions on Multimedia.'
+venue: "Journal 1"
+paperurl: "https://KevinXu-01.github.io/home/files/paper2.pdf"
+citation: "Jingyu Xu, Zechao Hu, Hao Li, et al. User Plays a Role: User-insight Multi-modal Recommendation. IEEE Transactions on Multimedia. 2024."
 ---
 
-Multi-Modal Recommendation (MMRec) aims to help users explore their potential interested items based on multi-modal information input and has been widely used in e-commerce platforms. Recent works mainly focus on modeling item-side information. However, they ignore the abundant semantic information from the user-information modeling, including age, gender, feedback, etc. Such imbalanced attention to item and user leads to inadequate expressiveness of comprehensive interests. 
-In this paper, we propose a novel \textbf{U}ser-\textbf{i}nsight \textbf{M}ulti-modal recommendation framework, termed UiM.
-This framework improves user modeling in three aspects:
-Firstly, to better explore the primary interests from a large-scale item pool, we propose to construct an enriched user profile to re-distribute attention to users' historical interactions.
-Secondly, to further disentangle compact representations from heterogeneous items, we propose to apply multi-interest feature extraction on re-attentioned item features.
-Moreover, an intrinsic shortage of a trivial recommender system is that it fails to access user feedback for in-place result adjustment. As a solution, we access pseudo feedback beforehand from an intelligent agent, then accordingly perform potential adjustments to recommendation candidates for finer results.
-Extensive experiments show that our model outperforms state-of-the-art multi-modal recommendation models in three public datasets.
+Multi-Modal Recommendation (MMRec) aims to help users explore their potential interested items based on multi-modal information input and has been widely used in e-commerce platforms. Recent works mainly focus on modeling item-side information. However, they ignore the abundant semantic information from the user-information modeling, including age, gender, feedback, etc. Such imbalanced attention to item and user leads to inadequate expressiveness of comprehensive interests. In this paper, we propose a novel User-insight Multi-modal recommendation framework, termed UiM. This framework improves user modeling in three aspects: Firstly, to better explore the primary interests from a large-scale item pool, we propose to construct an enriched user profile to re-distribute attention to users' historical interactions. Secondly, to further disentangle compact representations from heterogeneous items, we propose to apply multi-interest feature extraction on re-attentioned item features. Moreover, an intrinsic shortage of a trivial recommender system is that it fails to access user feedback for in-place result adjustment. As a solution, we access pseudo feedback beforehand from an intelligent agent, then accordingly perform potential adjustments to recommendation candidates for finer results. Extensive experiments show that our model outperforms state-of-the-art multi-modal recommendation models in three public datasets.