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Found 24 papers, 17 papers with code
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.
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment.
Scaling Law of Large Sequential Recommendation Models
We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea.
Reciprocal Sequential Recommendation
Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties.
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.
Multi-grained Hypergraph Interest Modeling for Conversational Recommendation
In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
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.
Privacy-Preserved Neural Graph Similarity Learning
To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.
Modeling Two-Way Selection Preference for Person-Job Fit
To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
Towards Universal Sequence Representation Learning for Recommender Systems
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information.
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session.
Leveraging Search History for Improving Person-Job Fit
As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.
Neural Graph Matching for Pre-training Graph Neural Networks
In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.
