BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System

• URL: http://arxiv.org/abs/2106.10898v2
• Date: Wed, 23 Jun 2021 07:30:43 GMT
• Title: BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System
• Authors: Shenghao Xu
• Abstract summary: Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. collaborative filtering (CF) is arguably the earliest and most influential method in the recommender system. BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. Due to the superior performance and low feedback learning without the learning to act in multiple situations, Multi-armed Bandits drawing widespread attention in applications ranging such as recommender systems. Likewise, within the recommender system, collaborative filtering (CF) is arguably the earliest and most influential method in the recommender system. Crucially, new users and an ever-changing pool of recommended items are the challenges that recommender systems need to address. For collaborative filtering, the classical method is training the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences which is the so-called cold start. So how to effectively recommend items to users in the absence of effective information? To address the aforementioned problems, a multi-armed bandit based collaborative filtering recommender system has been proposed, named BanditMF. BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition of scarcity of valid information, (2) how to solve the sub-optimal problem of bandit algorithms in strong social relations domains caused by independently estimating unknown parameters associated with each user and ignoring correlations between users.

Related papers

• An Efficient Multi-threaded Collaborative Filtering Approach in Recommendation System [0.0]
  This research focuses on building a scalable recommendation system capable of handling numerous users efficiently. A multithreaded similarity approach is employed to achieve this, where users are divided into independent threads that run in parallel. This parallelization significantly reduces computation time compared to traditional methods, resulting in a faster, more efficient, and scalable recommendation system.
  arXiv  Detail & Related papers  (2024-09-28T06:33:18Z)
• The Nah Bandit: Modeling User Non-compliance in Recommendation Systems [2.421459418045937]
  Expert with Clustering (EWC) is a hierarchical approach that incorporates feedback from both recommended and non-recommended options to accelerate user preference learning. EWC outperforms both supervised learning and traditional contextual bandit approaches. This work lays the foundation for future research in Nah Bandit, providing a robust framework for more effective recommendation systems.
  arXiv  Detail & Related papers  (2024-08-15T03:01:02Z)
• Managing multi-facet bias in collaborative filtering recommender systems [0.0]
  Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases.
  arXiv  Detail & Related papers  (2023-02-21T10:06:01Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach [56.12815715932561]
  We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
  arXiv  Detail & Related papers  (2022-04-20T01:25:08Z)
• Modeling Attrition in Recommender Systems with Departing Bandits [84.85560764274399]
  We propose a novel multi-armed bandit setup that captures policy-dependent horizons. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types.
  arXiv  Detail & Related papers  (2022-03-25T02:30:54Z)
• GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation [0.0]
  We propose a recommender system method using a graph-based model associated with the similarity of users' ratings. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
  arXiv  Detail & Related papers  (2021-11-06T10:47:45Z)
• A Hybrid Recommender System for Recommending Smartphones to Prospective Customers [0.7310043452300736]
  Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. We propose a hybrid recommender system, which combines Alternative Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance.
  arXiv  Detail & Related papers  (2021-05-26T23:10:51Z)
• Modurec: Recommender Systems with Feature and Time Modulation [50.51144496609274]
  We propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings.
  arXiv  Detail & Related papers  (2020-10-13T09:18:33Z)
• Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback [62.997667081978825]
  We present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches.
  arXiv  Detail & Related papers  (2020-09-16T07:32:51Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.