Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation
Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible sequence modeling with autoregressive mode…
Collaborative FilteringDeep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network
Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often lack interpretability and fail to gener…
Collaborative FilteringConstructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation
In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs) have shown to be useful in this proces…
Collaborative FilteringImproving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests
This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of da…
Collaborative FilteringOn the Mechanisms of Collaborative Learning in VAE Recommenders
Variational Autoencoders (VAEs) are a powerful alternative to matrix factorization for recommendation. A common technique in VAE-based collaborative filtering (CF) consists in applying binary input masking to user intera…
Collaborative FilteringLearning to Fast Unrank in Collaborative Filtering Recommendation
Modern data-driven recommendation systems risk memorizing sensitive user behavioral patterns, raising privacy concerns. Existing recommendation unlearning methods, while capable of removing target data influence, suffer …
Collaborative FilteringRecommendation SystemsDenoised Recommendation Model with Collaborative Signal Decoupling
Although the collaborative filtering (CF) algorithm has achieved remarkable performance in recommendation systems, it suffers from suboptimal recommendation performance due to noise in the user-item interaction matrix. N…
Collaborative FilteringRecommendation SystemsPairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation
Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform. Collaborative filtering-based RSs rely …
Collaborative FilteringVectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering
Recommender systems often struggle with data sparsity and cold-start scenarios, limiting their ability to provide accurate suggestions for new or infrequent users. This paper presents a Graph Attention Network (GAT) base…
Collaborative FilteringMMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models
We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. …
Collaborative FilteringOptimized Distortion in Linear Social Choice
Social choice theory offers a wealth of approaches for selecting a candidate on behalf of voters based on their reported preference rankings over options. When voters have underlying utilities for these options, however,…
Collaborative FilteringRecommendation SystemsEnhance Large Language Models as Recommendation Systems with Collaborative Filtering
As powerful tools in Natural Language Processing (NLP), Large Language Models (LLMs) have been leveraged for crafting recommendations to achieve precise alignment with user preferences and elevate the quality of the reco…
Collaborative FilteringRecommendation SystemsDoes Weighting Improve Matrix Factorization for Recommender Systems?
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interaction…
Collaborative FilteringFrom What to Why: Thought-Space Recommendation with Small Language Models
Large Language Models (LLMs) have advanced recommendation capabilities through enhanced reasoning, but pose significant challenges for real-world deployment due to high inference costs. Conversely, while Small Language M…
Sequential RecommendationCollaborative FilteringQuestion AnsweringBridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Recent research has explored using Large Language Models for recommendation tasks by transforming user interaction histories and item metadata into text prompts, then having the LLM produce rankings or recommendations. A…
Collaborative FilteringCatalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation
While collaborative filtering delivers predictive accuracy and efficiency, and Large Language Models (LLMs) enable expressive and generalizable reasoning, modern recommendation systems must bring these strengths together…
Collaborative FilteringRecommendation SystemsUnderstanding Generative Recommendation with Semantic IDs from a Model-scaling View
Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborativ…
Collaborative FilteringAccurate Predictions in Education with Discrete Variational Inference
One of the largest drivers of social inequality is unequal access to personal tutoring, with wealthier individuals able to afford it, while the majority cannot. Affordable, effective AI tutors offer a scalable solution. …
Collaborative FilteringFed-PISA: Federated Voice Cloning via Personalized Identity-Style Adaptation
Voice cloning for Text-to-Speech (TTS) aims to generate expressive and personalized speech from text using limited data from a target speaker. Federated Learning (FL) offers a collaborative and privacy-preserving framewo…
Collaborative FilteringFederated LearningAn Outcome-Based Educational Recommender System
Most educational recommender systems are tuned and judged on click- or rating-based relevance, leaving their true pedagogical impact unclear. We introduce OBER-an Outcome-Based Educational Recommender that embeds learnin…
Collaborative Filtering