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Found 25 papers, 17 papers with code
Recommendation with Generative Models
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions.
Multi-modal Generative Models in Recommendation System
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance.
Large Language Model Driven Recommendation
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unlocked the use of natural language (NL) interactions for recommendation.
Auto-Encoding or Auto-Regression? A Reality Check on Causality of Self-Attention-Based Sequential Recommenders
We start by tracing the AE/AR debate back to its origin through a systematic re-evaluation of SASRec and BERT4Rec, discovering that AR models generally surpass AE models in sequential recommendation.
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
The RTA framework marries the benefits of both LLMs and traditional recommender systems (RecSys): understanding complex queries as LLMs do; while efficiently controlling the recommended item distributions in conversational recommendations as traditional RecSys do.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) typically use user-item rating histories as their main data source.
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation
Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems.
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Based on the retrieved user-item interactions, the LLM can analyze shared and distinct preferences among users, and summarize the patterns indicating which types of users would be attracted by certain items.
RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface
We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS).
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
This fine-tuning allows the model to generate explanations that convey the compatibility relationships between items.
Linear Recurrent Units for Sequential Recommendation
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models.
Automatic Feature Fairness in Recommendation via Adversaries
We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model.
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
Generative Flow Network for Listwise Recommendation
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives.
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process.
Bundle MCR: Towards Conversational Bundle Recommendation
MCR, which uses a conversational paradigm to elicit user interests by asking user preferences on tags (e. g., categories or attributes) and handling user feedback across multiple rounds, is an emerging recommendation setting to acquire user feedback and narrow down the output space, but has not been explored in the context of bundle recommendation.
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations.
Leashing the Inner Demons: Self-Detoxification for Language Models
Language models (LMs) can reproduce (or amplify) toxic language seen during training, which poses a risk to their practical application.
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction
Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation.
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
MEAL: Multi-Model Ensemble via Adversarial Learning
In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN.
NAIS: Neural Attentive Item Similarity Model for Recommendation
As such, the key to an item-based CF method is in the estimation of item similarities.
Adversarial Personalized Ranking for Recommendation
Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.
