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Found 32 papers, 12 papers with code
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA, GPT, Mistral, and CLIP.
Large Language Model driven Policy Exploration for Recommender Systems
Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP).
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
To overcome this, we developed IISAN-Versa, a versatile plug-and-play architecture compatible with both symmetrical and asymmetrical MFMs.
Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models
Advancements in Natural Language Processing (NLP), have led to the emergence of Large Language Models (LLMs) such as GPT, Llama, Claude, and Gemini, which excel across a range of tasks but require extensive fine-tuning to align their outputs with human expectations.
PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences.
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling
The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions.
Latent User Intent Modeling for Sequential Recommenders
We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.
Supervised Advantage Actor-Critic for Recommender Systems
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
Enhancing Top-N Item Recommendations by Peer Collaboration
In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
On Interpretation and Measurement of Soft Attributes for Recommendation
We address how to robustly interpret natural language refinements (or critiques) in recommender systems.
Graph Convolutional Embeddings for Recommender Systems
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
Untangle: Critiquing Disentangled Recommendations
Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.
One Person, One Model, One World: Learning Continual User Representation without Forgetting
In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.
Self-Supervised Reinforcement Learning for Recommender Systems
A major component of RL approaches is to train the agent through interactions with the environment.
Graph Highway Networks
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.
A Simple Convolutional Generative Network for Next Item Recommendation
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
Towards a universal neural network encoder for time series
We evaluate the performance of the proposed approach on a well-known time series classification benchmark, considering full adaptation, partial adaptation, and no adaptation of the encoder to the new data type.
Overcoming catastrophic forgetting with hard attention to the task
In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning.
Ranked #2 on
Continual Learning
on 20Newsgroup (10 tasks)
Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks
Due to the structure of the data coming from recommendation domains (i. e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
Hot or not? Forecasting cellular network hot spots using sector performance indicators
Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots.
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.
Graph Clustering Bandits for Recommendation
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users.
Session-based Recommendations with Recurrent Neural Networks
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
Ranked #2 on
Recommendation Systems
on MovieLens 20M
(nDCG@10 (full corpus) metric)
A genetic algorithm to discover flexible motifs with support
Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif.
Collaborative Filtering Bandits
Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users.
