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Unbiasing Collaborative Filtering for Popularity-Aware Recommendation (Discussion Paper)

Luciano Caroprese, Giuseppe Manco, Marco Minici, Francesco Sergio Pisani, Ettore Ritacco
2021 Sistemi Evoluti per Basi di Dati  
popularity of the items to recommend.  ...  Our findings show that most popular ranking-based recommenders are biased towards popular items, thus affecting the quality of recommendation.  ...  Conclusions The approach proposed in this paper is a preliminary study: We introduce a Ranking collaborative filtering algorithm (RVAE) and study how the algorithm is affected by popularity bias.  ... 
dblp:conf/sebd/Caroprese0MPR21 fatcat:xrlbx6ae2rgvtavwbz6tl5ye6e

Fostering Discussion across Communication Media in Massive Open Online Courses

Oliver Ferschke, Iris K. Howley, Gaurav Tomar, Diyi Yang, Yu Liu, Carolyn Penstein Rosé
2015 International Conference on Computer Supported Collaborative Learning  
This paper presents data from one cycle of a design based research process in which we grapple with challenges in engaging students in more intensive discussion based interactions i n M a s s i v e O p  ...  The analysis suggests that there is value in providing a diverse set of discussion contexts in that they may lend themselves to differently natured interactions, but that it creates a need for greater  ...  Acknowledgements This research was funded in part by NSF Grants DATANET 1443068, IIS-1320064, and OMA-0836012 as well as a collaborative grant with Google.  ... 
dblp:conf/cscl/FerschkeHTYLR15 fatcat:m4aarst57nhy3pimn2wp5y5tti

Causal Inference in Recommender Systems: A Survey and Future Directions [article]

Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
2022 arXiv   pre-print
Existing recommender systems extract the user preference based on learning the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation  ...  For example, the recommender systems can recommend a battery charger to a user after buying a phone, in which the latter can serve as the cause of the former, and such a causal relation cannot be reversed  ...  For popularity bias or exposure bias, the bias (due to popularity-aware or exposure strategy-aware data collection) can be regarded as a kind of confounder in most cases.  ... 
arXiv:2208.12397v1 fatcat:jpjp5sjunvczhmikx5gfjvc3qq

Deconfounded Causal Collaborative Filtering [article]

Shuyuan Xu and Juntao Tan and Shelby Heinecke and Jia Li and Yongfeng Zhang
2021 arXiv   pre-print
To solve the problem, we propose a deconfounded causal collaborative filtering model.  ...  Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance.  ...  Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.  ... 
arXiv:2110.07122v1 fatcat:2ytfb42y6bcndopzvs4gp3455m

A Systematic Study on a Customer's Next-Items Recommendation Techniques

Qazi Mudassar Ilyas, Abid Mehmood, Ashfaq Ahmad, Muneer Ahmad
2022 Sustainability  
To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing  ...  The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past.  ...  The most popular techniques in this category include Markov chains, collaborative filtering, and knearest neighbors.  ... 
doi:10.3390/su14127175 fatcat:fydrheys3zd4fesibokorcprqq

SocialRec: A Context-aware Recommendation Framework with Explicit Sentiment Analysis

Rizwana Irfan, Osman Khalid, Muhammad U. S. Khan, Faisal Rehman, Atta Ur Rehman Khan, Raheel Nawaz
2019 IEEE Access  
In this paper, we propose SocialRec, a hybrid context-aware recommendation framework that utilizes a rating inference approach to incorporate users' textual reviews into traditional collaborative filtering  ...  INDEX TERMS Text mining, recommendation system, collaborative filtering, Hub-Average inference.  ...  The authors in [27] proposed a collaborative sequential map filtering algorithm for E-learning.  ... 
doi:10.1109/access.2019.2932500 fatcat:tnlp3rdcnva37og7wnzbm7lee4

Recommendation Systems for News Articles at the BBC

Maria Panteli, Alessandro Piscopo, Adam Harland, Jonathan Tutcher, Felix Mercer Moss
2019 ACM Conference on Recommender Systems  
In this paper we describe how we develop recommendation systems for news articles at the BBC.  ...  When it comes to news recommendations, and especially for a public service broadcaster like the BBC, recommendation systems need to be in line with the editorial policy and the business values of the organisation  ...  Cosine-based collaborative filtering. The second approach was a combination of simple user-item collaborative filtering and a session-based approach.  ... 
dblp:conf/recsys/PanteliPHTM19 fatcat:pnbdhvdu2bdzdafq2dpga2vria

Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Network Projection

Fengjing Yin, Xiang Zhao, Xin Zhang, Bin Ge, Weidong Xiao
2014 Mathematical Problems in Engineering  
To enhance the performance, this paper devises a negative-aware and rating-integrated algorithm on top of the baseline algorithm.  ...  Bipartite network projection method has been recently employed for personal recommendation. It constructs a bipartite network between users and items.  ...  A scalable online collaborative filtering algorithm for news recommendation is proposed in [22] , which combines memory based and model based collaborative filtering algorithm.  ... 
doi:10.1155/2014/823749 fatcat:b7zy4ed7k5davc7ytjgozpuhsm

Bias and Debias in Recommender System: A Survey and Future Directions [article]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He
2021 arXiv   pre-print
While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data.  ...  In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics.  ...  A research direction is to understand the dimensions of causality and design fairness-aware collaborative filtering algorithms in case sensitive attributes are not readily available.  ... 
arXiv:2010.03240v2 fatcat:6fticc3otndsra2whs5e4nrdpi

Incorporating Distinct Opinions in Content Recommender System [chapter]

Grace E. Lee, Keejun Han, Mun Y. Yi
2015 Lecture Notes in Computer Science  
To ensure robustness, we develop four new hybrid methods that are various mixtures of existing collaborative filtering (CF) methods and our new measure of Distinctness.  ...  In this paper, we present a new measure to detect opinions that are distinct from the mainstream.  ...  Conclusion and Future Work The goal of this paper is to suggest a new collaborative filtering method for content recommender systems.  ... 
doi:10.1007/978-3-319-28940-3_9 fatcat:sfx6upavp5bgxla4sthzibdtmy

Hybrid recommender systems: A systematic literature review

Erion Çano, Maurizio Morisio
2017 Intelligent Data Analysis  
Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way.  ...  Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies.  ...  This technique is popular among collaborative filtering RSs which represent the most common family of recommenders.  ... 
doi:10.3233/ida-163209 fatcat:rqskvan7lrhmtcncsid2dpdata

A Proposed Business Intelligent Framework for Recommender Systems

Sitalakshmi Venkatraman
2017 Informatics  
a challenge for recommender systems.  ...  In this Internet age, recommender systems (RS) have become popular, offering new opportunities and challenges to the business world.  ...  The author also thanks the reviewers and editor for their invaluable comments and support rendered for this paper.  ... 
doi:10.3390/informatics4040040 fatcat:gjt7bfcddjdiphfgwgotrbfxlq

A Brief History of Recommender Systems [article]

Zhenhua Dong, Zhe Wang, Jun Xu, Ruiming Tang, Jirong Wen
2022 arXiv   pre-print
services for changing the world better.  ...  This article briefly reviews the history of web recommender systems, mainly from two aspects: (1) recommendation models, (2) architectures of typical recommender systems.  ...  John Riedl for his great and pioneering research in area of recommender systems, and his wonderful conversations with Zhenhua Dong about GroupLens, MovieLens, Net Perception during 2010 to 2011, there  ... 
arXiv:2209.01860v1 fatcat:dvmrna4orjhhxdvrdkon57n54y

Benchmarking News Recommendations in a Living Lab [chapter]

Frank Hopfgartner, Benjamin Kille, Andreas Lommatzsch, Till Plumbaum, Torben Brodt, Tobias Heintz
2014 Lecture Notes in Computer Science  
In this paper, we introduce a living lab on news recommendation in real time.  ...  We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems.  ...  Acknowledgement The work leading to these results has received funding (or partial funding) from the Central Innovation Programme for SMEs of the German Federal Ministry for Economic Affairs and Energy  ... 
doi:10.1007/978-3-319-11382-1_21 fatcat:2a25ii4wpbeldb27jnax5ewbzq

Neural Collaborative Autoencoder [article]

Qibing Li, Xiaolin Zheng, Xinyue Wu
2018 arXiv   pre-print
To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit  ...  Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy.  ...  learning and collaborative filtering.  ... 
arXiv:1712.09043v3 fatcat:7wdjtnj7lndy5eatpixngnldwm
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