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Found 32 papers, 20 papers with code
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs).
Aligning Large Language Models for Controllable Recommendations
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable.
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps.
Aligning Language Models for Versatile Text-based Item Retrieval
Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks.
Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
However, most existing retrieval models employ a single-round inference paradigm, which may not adequately capture the dynamic nature of user preferences and stuck in one area in the item space.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
RecExplainer: Aligning Large Language Models for Explaining Recommendation Models
Behavior alignment operates in the language space, representing user preferences and item information as text to mimic the target model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces.
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
In this paper, we propose a general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems
Recommendation systems effectively guide users in locating their desired information within extensive content repositories.
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system.
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
Towards Better Entity Linking with Multi-View Enhanced Distillation
Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
Distillation from Heterogeneous Models for Top-K Recommendation
Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.
Geometric Interaction Augmented Graph Collaborative Filtering
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
We first extract distribution patterns from the item candidates.
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Then we propose a hierarchical CB (HCB) algorithm to explore users' interest in the hierarchy tree.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
Self-supervised Graph Learning for Recommendation
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
Ranked #6 on
Collaborative Filtering
on Yelp2018
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data.
MIND: A Large-scale Dataset for News Recommendation
News recommendation is an important technique for personalized news service.
Lightrec: A memory and search-efficient recommender system
On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.
Graph Convolution Machine for Context-aware Recommender System
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.
KRED: Knowledge-Aware Document Representation for News Recommendations
News articles usually contain knowledge entities such as celebrities or organizations.
A Novel User Representation Paradigm for Making Personalized Candidate Retrieval
Our work is undergoing a anonymous review, and it will soon be released after the notification.
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
User modeling is an essential task for online rec- ommender systems.
Ranked #2 on
Recommendation Systems
on Amazon Product Data
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
Ranked #1 on
Click-Through Rate Prediction
on Dianping
