Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method
This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link disjoint users and items across different information domains and leverage them to make cross-domain content-based and…
Collaborative FilteringInformation RetrievalRetrievalRepresentation Extraction and Deep Neural Recommendation for Collaborative Filtering
Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works have investigated the application of de…
Collaborative FilteringRepresentation LearningProbabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix …
Bayesian InferenceCollaborative FilteringVariational InferenceResearch Progress of News Recommendation Methods
Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earl…
Collaborative FilteringKnowledge GraphsNews RecommendationRecommendation SystemsOn Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering
Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand b…
AttributeBIG-bench Machine LearningCausal InferenceCollaborative Filtering+2Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Collaborative filtering has been widely used in recommender systems. Existing work has primarily focused on improving the prediction accuracy mainly via either building refined models or incorporating additional side inf…
Collaborative FilteringRecommendation SystemsFair Multi-Stakeholder News Recommender System with Hypergraph ranking
Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggregator website users, authors, magazines a…
Collaborative FilteringRecommendation SystemsUnbiasing Review Ratings with Tendency Based Collaborative Filtering
User-generated contents{'} score-based prediction and item recommendation has become an inseparable part of the online recommendation systems. The ratings allow people to express their opinions and may affect the market …
Collaborative FilteringPredictionRecommendation SystemsBERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation
We propose a novel, accurate, and explainable recommender model (BENEFICT) that addresses two drawbacks that most review-based recommender systems face. First is their utilization of traditional word embeddings that coul…
Collaborative FilteringRecommendation SystemsWord EmbeddingsEmbedding Semantic Taxonomies
A common step in developing an understanding of a vertical domain, e.g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain. These human created artifacts have been the s…
ArticlesCollaborative FilteringCOVID-19: A Semantic-Based Pipeline for Recommending Biomedical Entities
With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedic…
Collaborative Filteringnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study
This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shop…
Collaborative FilteringRecommendation SystemsThe complementarity of a diverse range of deep learning features extracted from video content for video recommendation
Following the popularisation of media streaming, a number of video streaming services are continuously buying new video content to mine the potential profit from them. As such, the newly added content has to be handled w…
Collaborative FilteringDeep LearningRecommendation SystemsRecommendation Systems (Item cold-start)Interest Sustainability-Aware Recommender System
The key to successful recommendations is to provide users with items likely to be consumed in the future. From realworld data, we observe that users' consumption patterns for items change over time. For example, users ma…
Collaborative FilteringRecommendation SystemsA Probability Distribution and Location-aware ResNet Approach for QoS Prediction
In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among h…
Collaborative FilteringPredictionHeterogeneous Graph Collaborative Filtering
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite graph, where users and items are two isolate…
Collaborative FilteringLearning User Representations with Hypercuboids for Recommender Systems
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is …
Collaborative FilteringRecommendation SystemsTRSM-RS: A Movie Recommender System Based on Users' Gender and New Weighted Similarity Measure
With the growing data on the Internet, recommender systems have been able to predict users' preferences and offer related movies. Collaborative filtering is one of the most popular algorithms in these systems. The main p…
Collaborative FilteringRecommendation SystemsEcho Chambers in Collaborative Filtering Based Recommendation Systems
Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to…
Collaborative FilteringDiversityRecommendation SystemsDeoscillated Graph Collaborative Filtering
Collaborative Filtering (CF) signals are crucial for a Recommender System~(RS) model to learn user and item embeddings. High-order information can alleviate the cold-start issue of CF-based methods, which is modelled thr…
Collaborative FilteringRecommendation Systems