Tootfinder

Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering
Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park
https://arxiv.org/abs/2405.00287

Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering
Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochast…

KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
https://arxiv.org/abs/2403.20095

KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph Collaborative Filtering (KGCF), which use KGs to…

Online Policy Learning and Inference by Matrix Completion
Congyuan Duan, Jingyang Li, Dong Xia
https://arxiv.org/abs/2404.17398 https://

Online Policy Learning and Inference by Matrix Completion
Making online decisions can be challenging when features are sparse and orthogonal to historical ones, especially when the optimal policy is learned through collaborative filtering. We formulate the problem as a matrix completion bandit (MCB), where the expected reward under each arm is characterized by an unknown low-rank matrix. The $ε$-greedy bandit and the online gradient descent algorithm are explored. Policy learning and regret performance are studied under a specific schedule for explor…

Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
Hoin Jung, Hyunsoo Cho, Myungje Choi, Joowon Lee, Jung Ho Park, Myungjoo Kang
https://arxiv.org/abs/2404.17598

Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and un…

Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
Ziyi Liang, Tianmin Xie, Xin Tong, Matteo Sesia
https://arxiv.org/abs/2404.17561

Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
We develop a conformal inference method to construct joint confidence regions for structured groups of missing entries within a sparsely observed matrix. This method is useful to provide reliable uncertainty estimation for group-level collaborative filtering; for example, it can be applied to help suggest a movie for a group of friends to watch together. Unlike standard conformal techniques, which make inferences for one individual at a time, our method achieves stronger group-level guarantees …

This https://arxiv.org/abs/2306.05292 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Safe Collaborative Filtering
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalized recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a "safe" collaborative filtering method that prioritizes recommendation quality for less-sat…

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
https://arxiv.org/abs/2403.17416

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…

This https://arxiv.org/abs/2403.18479 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Lightweight Embeddings for Graph Collaborative Filtering
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recommenders have inherited the long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (i.e., meta-embeddings). When assigning meta-embeddings, most existing methods are a heuristically …

This https://arxiv.org/abs/2305.02759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Disentangled Contrastive Collaborative Filtering
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact…

This https://arxiv.org/abs/2204.11602 has been replaced.
link: https://scholar.google.com/scholar?q=a

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad C…

Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
Yu Hou, Jin-Duk Park, Won-Yong Shin
https://arxiv.org/abs/2404.14240

Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based recommender systems do not explicitly leverage high-order connectivities that contain crucial collaborative signals for accurate recommendations. Addressing this gap, we propose CF-Diff, a new diffusion model-based collaborative filtering (CF) method, which is capable of making f…

Large Language Models Enhanced Collaborative Filtering
Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, Jun Xu
https://arxiv.org/abs/2403.17688

Large Language Models Enhanced Collaborative Filtering
Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing work predominantly utilizes LLMs to generate knowledge-rich texts or utilizes LLM-derived embeddings as features to improve RSs. Al- though the extensive world knowledge embedded in LLMs generally benefits RSs, the application can only take limited number of users and items as inputs, without adequately exploiting coll…

This https://arxiv.org/abs/2311.08744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Graph Signal Diffusion Model for Collaborative Filtering
Collaborative filtering is a critical technique in recommender systems. Among various methods, an increasingly popular paradigm is to reconstruct user-item interactions based on the historical observations. This can be viewed as a conditional generative task, where recently developed diffusion model demonstrates great potential. However, existing studies on diffusion models lack effective solutions for modeling implicit feedback data. Particularly, the isotropic nature of the standard diffusion…

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang
https://arxiv.org/abs/2402.13769

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood information on a user-item interaction graph to enhance user/item representations. However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph. For instance, a user's interactions with items can be driven by both unbiased true interest and variou…

Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
Jin-Duk Park, Yong-Min Shin, Won-Yong Shin
https://arxiv.org/abs/2404.14243 htt…

Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly perform matrix decomposition on the item-item similarity graph to realize the ideal LPF, which results in a non-trivial computational cost and thus makes them less practical in scenarios where rapid recommendations are essential. In this paper, we …

This https://arxiv.org/abs/2404.15954 has been replaced.
link: https://scholar.google.com/scholar?q=a

Mixed Supervised Graph Contrastive Learning for Recommendation
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss. This decoupled design can cause inconsistent optimization direction from …

Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems
Aixin Sun
https://arxiv.org/abs/2404.13375 https://

Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems
Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the formulation of research tasks from real-world contexts, aiming for a clean problem formulation and more generalizable findings. However, it is observed that there is a lack of collective understanding in RecSys academic research. The root of this issue may lie in the simplification …

Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, Chanyoung Park
https://arxiv.org/abs/2404.11343

Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecSys struggles under cold scenarios with sparse user-item interactions, recent strategies have focused on leveraging modality information of user/items (e.g., text or images) based on pre-trained modality encoders and Large Language Models (LLMs). Despite their effectiveness under cold scenarios, we observe that they un…

This https://arxiv.org/abs/2211.13912 has been replaced.
link: https://scholar.google.com/scholar?q=a

Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that suit these models, however, remain underdeveloped. One challenge is that existing hard negative samplers tend to suffer from severer over-fitting in model training. In this work, we first study the reason behind the over-fitting, and illustrate it with the inco…

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
Kexin Shi, Jing Zhang, Linjiajie Fang, Wenjia Wang, Bingyi Jing
https://arxiv.org/abs/2403.19276

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative sampling, as these false negatives can provide incorrect information and mislead the model learning. To date, only a small number of studies have been committed to solve the false negative problem, primarily focusing on designing sophisticated sampling algorit…

This https://arxiv.org/abs/2311.13277 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Hierarchical Matrix Factorization for Interpretable Collaborative Filtering
Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user-item interaction matrix into user and item latent matrices. Because the model typically learns each interaction independently, it may overlook the underlying shared dependencies between users and items, resulting in less stable and interpretable recommendations. Based on these insights, we propose "Hierarchical Matrix Factorization" (HMF), which incorpor…

This https://arxiv.org/abs/2403.17740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the …

Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Haoxuan Li, Yuanxin Ouyang, Zhuang Liu, Wenge Rong, Zhang Xiong
https://arxiv.org/abs/2404.05962

Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. In this paper, we propose a novel approach, called the Wasserstein dependent Graph ATtention network (W-GAT), for collaborative filtering with uncertainty. We utilize graph attention network and Wasserst…

Collaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
Nanna Bach Munkholm, Robert Alphinas, Torben Tambo
https://arxiv.org/abs/2402.06233

Collaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
An architectural framework, based on collaborative filtering using K-nearest neighbor and cosine similarity, was developed and implemented to fit the requirements for the company DecorRaid. The aim of the paper is to test different evaluation techniques within the environment to research the recommender systems performance. Three perspectives were found relevant for evaluating a recommender system in the specific environment, namely dataset, system and user perspective. With these perspectives …

This https://arxiv.org/abs/2403.17416 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…

Self-supervised Contrastive Learning for Implicit Collaborative Filtering
Shipeng Song, Bin Liu, Fei Teng, Tianrui Li
https://arxiv.org/abs/2403.07265 http…

Self-supervised Contrastive Learning for Implicit Collaborative Filtering
Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering. However, the presence of false-positive and false-negative examples in recommendation systems hampers accurate preference learning. In this study, we propose a simple self-supervised contrastive learning framework that leverages positive feature augmentation…

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Shuheng Fang, Kangfei Zhao, Yu Rong, Zhixun Li, Jeffrey Xu Yu
https://arxiv.org/abs/2403.17740

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the …

Advancing Recommender Systems by mitigating Shilling attacks
Aditya Chichani, Juzer Golwala, Tejas Gundecha, Kiran Gawande
https://arxiv.org/abs/2404.16177

Advancing Recommender Systems by mitigating Shilling attacks
Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shill…

This https://arxiv.org/abs/2307.04384 has been replaced.
link: https://scholar.google.com/scholar?q=a

Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn user and item embeddings with Graph Convolutional Network (GCN) and utilize these embeddings for CF models. However, existing GCN-based methods are insufficient in generating satisfactory embeddings for CF models. This is because they fail to model complex node dependencies and v…

Frequency-aware Graph Signal Processing for Collaborative Filtering
Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu
https://arxiv.org/abs/2402.08426

Frequency-aware Graph Signal Processing for Collaborative Filtering
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item characteristics and failed to utilize user and item high-order neighborhood information to model user preference, thus leading to sub-optimal performance. To address the above issues, we propose a frequency-aware graph signal processing method (FaGSP) for …

Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
Jiaheng Yu, Jing Li, Yue He, Kai Zhu, Shuyi Zhang, Wen Hu
https://arxiv.org/abs/2403.15075

Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation, which limits existing models to achieve ideal results. To solve this issue, we propose a nove…

This https://arxiv.org/abs/2310.09400 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Collaborative Semantic Alignment in Recommendation Systems
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced rich semantic modeling of item descriptions. However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions. Our work introduces CARec, a cut…

MARec: Metadata Alignment for cold-start Recommendation
Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder, Anton van den Hengel
https://arxiv.org/abs/2404.13298

MARec: Metadata Alignment for cold-start Recommendation
For many recommender systems the primary data source is a historical record of user clicks. The associated click matrix which is often very sparse, however, as the number of users x products can be far larger than the number of clicks, and such sparsity is accentuated in cold-start settings. The sparsity of the click matrix is the reason matrix factorization and autoencoders techniques remain highly competitive across collaborative filtering datasets. In this work, we propose a simple approach …

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Feiran Huang, Zhenghang Yang, Junyi Jiang, Yuanchen Bei, Yijie Zhang, Hao Chen
https://arxiv.org/abs/2402.09176

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Recommending cold items is a long-standing challenge for collaborative filtering models because these cold items lack historical user interactions to model their collaborative features. The gap between the content of cold items and their behavior patterns makes it difficult to generate accurate behavioral embeddings for cold items. Existing cold-start models use mapping functions to generate fake behavioral embeddings based on the content feature of cold items. However, these generated embeddin…

Cluster-based Graph Collaborative Filtering
Fan Liu, Shuai Zhao, Zhiyong Cheng, Liqiang Nie, Mohan Kankanhalli
https://arxiv.org/abs/2404.10321 https://

Countering Mainstream Bias via End-to-End Adaptive Local Learning
Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, James Caverlee
https://arxiv.org/abs/2404.08887

Countering Mainstream Bias via End-to-End Adaptive Local Learning
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstre…

This https://arxiv.org/abs/2206.02631 has been replaced.
link: https://scholar.google.com/scholar?q=a

Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the progression of personalized recommendation methodologies tailored for online products or services, organizing the array of recommendation techniques into four main categories: content-based, collaborative filtering, knowledge-based, and hybrid approaches, each designed to cater …

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Yining Wu, Shengyu Duan, Gaole Sai, Chenhong Cao, Guobing Zou
https://arxiv.org/abs/2404.04265

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either putting in additional computational resources or utilizing parallel systems, intro…

This https://arxiv.org/abs/2308.02058 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Incorporating Recklessness to Collaborative Filtering based Recommender Systems
Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This leads to a significant drop in the novelty of these systems, since instead of recommending uncertain unusual items, they focus on predicting items with guaranteed success. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based rec…

This https://arxiv.org/abs/2206.02631 has been replaced.
link: https://scholar.google.com/scholar?q=a

Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the progression of personalized recommendation methodologies tailored for online products or services, organizing the array of recommendation techniques into four main categories: content-based, collaborative filtering, knowledge-based, and hybrid approaches, each designed to cater …

This https://arxiv.org/abs/2206.02631 has been replaced.
link: https://scholar.google.com/scholar?q=a

Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the progression of personalized recommendation methodologies tailored for online products or services, organizing the array of recommendation techniques into four main categories: content-based, collaborative filtering, knowledge-based, and hybrid approaches, each designed to cater …