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- Video7.7 MBPublished By ACM
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1)noisy multi-modal content, (2) noisy user feedback, and (...
- Video15.4 MBPublished By ACM
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike, low ...
- Video767.4 MBPublished By ACM
Multimodal Conditioned Diffusion Model for Recommendation
Multimodal recommendation aims at to modeling the feature distributions of items by using their multi-modal information. Prior efforts typically focus on the denoising of the user-item graph with a degree-sensitive strategy, which may not well-handle the ...
- Video1.3 GBPublished By ACM
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such ...
- Video98.9 MBPublished By ACM
Multi-granularity Fatigue in Recommendation
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPersonalized recommendation aims to provide appropriate items according to user preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar items will lead to fatigue, which may decrease user activeness and degrade user ...
- Video260.9 MBPublished By ACM
Contrastive Cross-domain Recommendation in Matching
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., candidate generation) ...
- Video67.8 MBPublished By ACM
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect information ...
- Video85.2 MBPublished By ACM
Selective Fairness in Recommendation via Prompts
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those ...
- Video117.5 MBPublished By ACM
A Peep into the Future: Adversarial Future Encoding in Recommendation
Personalized recommendation often relies on user historical behaviors to provide items for users. It is intuitive that future information also contains essential messages as supplements to user historical behaviors. However, we cannot directly encode ...
- Video70.4 MBPublished By ACM
Personalized Transfer of User Preferences for Cross-domain Recommendation
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's ...
- Video114.4 MBPublished By ACM
Long Short-Term Temporal Meta-learning in Online Recommendation
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks). However, it is extremely ...
- Video163.7 MBPublished By ACM
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementSearch and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing ...
- Video195.2 MBPublished By ACM
Explore, Filter and Distill: Distilled Reinforcement Learning in Recommendation
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementReinforcement learning (RL) has been verified in real-world list-wise recommendation. However, RL-based recommendation suffers from huge memory and computation costs due to its large-scale models. Knowledge distillation (KD) is an effective approach for ...
- Video95.1 MBPublished By ACM
Adversarial Feature Translation for Multi-domain Recommendation
Real-world super platforms such as Google and WeChat usually have different recommendation scenarios to provide heterogeneous items for users' diverse demands. Multi-domain recommendation (MDR) is proposed to improve all recommendation domains ...
- Video11.1 MBPublished By ACM
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of ...
- Video33.6 MBPublished By ACM
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem. Especially, for the cold-start item which only has limited interactions, ...
- Video155 MBPublished By ACM
Package Recommendation with Intra- and Inter-Package Attention Networks
With the booming of online social networks in the mobile internet, an emerging recommendation scenario has played a vital role in information acquisition for user, where users are no longer recommended with a single item or item list, but a combination ...
- Video09:44128.9 MBPublished By ACM
Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementIn tag-enhanced video recommendation systems, videos are attached with some tags that highlight the contents of videos from different aspects. Tag ranking in such recommendation systems provides personalized tag lists for videos from their tag ...
- Video05:0021.7 MBPublished By ACM
Curriculum Learning for Wide Multimedia-Based Transformer with Graph Target Detection
The social media prediction task is aiming at predicting content popularity which includes social multimedia data such as photos, videos, and news. The task can not only help make better decisions for recommendation, but also reveals the public ...