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CLiMF

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, Alan Hanjalic
2012 Proceedings of the sixth ACM conference on Recommender systems - RecSys '12  
To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF).  ...  In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring the performance of top-k recommendations  ...  In view of the drawbacks of previous work, we propose a new CF approach, Collaborative Less-is More Filtering (CLiMF), that is tailored to recommendation domains where only binary relevance data is available  ... 
doi:10.1145/2365952.2365981 dblp:conf/recsys/ShiKBLOH12 fatcat:fo2jdgessfbf7db5r24472iqoy

Exploiting Explicit and Implicit Feedback for Personalized Ranking

Gai Li, Qiang Chen
2016 Mathematical Problems in Engineering  
and implicit feedback simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR).  ...  ++ showed a linear correlation with the number of rating.  ...  Acknowledgments This work is sponsored in part by the National Natural  ... 
doi:10.1155/2016/2535329 fatcat:o7l3meksmbd4tbsaejgyzz2zoa

Collaborative Ranking with a Push at the Top

Konstantina Christakopoulou, Arindam Banerjee
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users.  ...  From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural.  ...  for helpful discussions related to the paper.  ... 
doi:10.1145/2736277.2741678 dblp:conf/www/Christakopoulou15 fatcat:ajjqg2h2dzfixhvamg267tsvdm

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems [article]

Junjie Liang, Jinlong Hu, Shoubin Dong, Vasant Honavar
2018 arXiv   pre-print
We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function.  ...  Then, a more efficient variant, Top-N-Rank.ReLU, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear  ...  Instead of directly optimizing MRR, CLiMF learns the latent factors by maximizing the smoothed lower bound of MRR; (iii) xCLiMF: An extension of CLiMF that optimizes the expected reciprocal rank (ERR),  ... 
arXiv:1812.04109v2 fatcat:nfwnneydkzgazbjdkpkyskwua4

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation [article]

Lei Chen, Le Wu, Kun Zhang, Richang Hong, Meng Wang
2021 arXiv   pre-print
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the  ...  For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown  ...  INTRODUCTION Collaborative Filtering (CF) provides personalized ranking list for each user by leveraging the collaborative signals from user-item interaction data, and are popular in most recommender systems  ... 
arXiv:2105.07377v2 fatcat:4vl3glcqorcgzlrjqse5bh3wda

CORALS

Ruirui Li, Jyun-Yu Jiang, Chelsea J.-T. Ju, Wei Wang
2019 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19  
To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets.  ...  This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.  ...  We would like to thank Shuo Song, Bin Bi, and Zijun Xue for their insightful comments and discussions. We also thank the reviewers for their constructive feedback.  ... 
doi:10.1145/3289600.3290995 dblp:conf/wsdm/LiJJW19 fatcat:e5c4emdylzfrnd5e7cutw3om5m

Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization

Yunhui Guo, Congfu Xu, Hanzhang Song, Xin Wang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable.  ...  So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products.  ...  Acknowledgments This research is supported by the National Natural Science Foundation of China (NSFC) No.61672449 and No.61472347.  ... 
doi:10.24963/ijcai.2017/247 dblp:conf/ijcai/GuoXSW17 fatcat:6znekymqgzdipn6cvmy2wdz3zq

A Survey of One Class E-Commerce Recommendation System Techniques

Mohamed Khoali, Yassin Laaziz, Abdelhak Tali, Habeeb Salaudeen
2022 Electronics  
To tackle the identified problems, we propose a neural network-based Bayesian Personalized Ranking (BPR) for item recommendation and personalized ranking from implicit feedback.  ...  Our approach shows an impressive result in mitigating the issues of one-class recommendation when compared with the complexity of the state-of-the-art methods.  ...  Collaborative Less-is-More Filtering (CLIMF) [17] is developed off Collaborative Filtering where model parameters are learned through direct maximization (optimization) of the Mean Reciprocal Rank which  ... 
doi:10.3390/electronics11060878 fatcat:i4c4izu7nbbxrcgcjmaddzuua4

Social Collaborative Viewpoint Regression with Explainable Recommendations

Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, Maarten de Rijke
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce.  ...  A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences.  ...  All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.  ... 
doi:10.1145/3018661.3018686 dblp:conf/wsdm/RenLLWR17 fatcat:57jeaewczbadnbjle6c6n3l6la

Listwise Collaborative Filtering

Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems.  ...  to previous ranking-oriented memory-based CF algorithms.  ...  BPR and CLiMF model the binary relevance data and optimize binary relevance metrics, i.e., Area Under the Curve (AUC) in BPR and Mean Reciprocal Rank (Mean) in CLiMF, which are not suited for graded relevance  ... 
doi:10.1145/2766462.2767693 dblp:conf/sigir/HuangWLMCV15 fatcat:3j55zjitzbfa5nrumqwb2zbwsy

GAPfm

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In order to ensure that GAPfm is able to scale to very large data sets, we propose a fast learning algorithm that uses an adaptive item selection strategy.  ...  If accurate top-N recommendation lists are to be produced for such graded relevance domains, it is critical to generate a ranked list of recommended items directly rather than predicting ratings.  ...  Bayesian personalized ranking (BPR) [25] and Collaborative Less-is-More Filtering (CLiMF) [29] seek to improve top-N recommendation by directly optimize binary relevance measures, i.e., Area Under  ... 
doi:10.1145/2505515.2505653 dblp:conf/cikm/ShiKBLH13 fatcat:4vsr343swzf35mydj2q57mpzui

CoFiSet: Collaborative Filtering via Learning Pairwise Preferences over Item-sets [chapter]

Weike Pan, Li Chen
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets).  ...  Collaborative filtering aims to make use of users' feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems.  ...  CLiMF (collaborative less-is-more filtering) [18] proposes to encourage self-competitions among observed items only via maximizing i∈I tr u ln σ(r ui ) + i ′ ∈I tr u \{i} ln σ(r ui −r ui ′ ) for each  ... 
doi:10.1137/1.9781611972832.20 dblp:conf/sdm/ChenP13 fatcat:pwpbtpcwnzcmnfpqk5uewauti4

Accurate and Diverse Recommendation based on Users' Tendencies toward Temporal Item Popularity

Koki Nagatani, Masahiro Sato
2017 ACM Conference on Recommender Systems  
Popularity bias is a phenomenon associated with collaborative filtering algorithms, in which popular items tend to be recommended over unpopular items.  ...  In this paper, we propose a novel approach to counteract the popularity bias, namely, matrix factorization based collaborative filtering incorporating individual users' tendencies toward item popularity  ...  For pair-wise optimization, area under the curve (AUC) in Bayesian personalized ranking [11] , mean reciprocal rank used in collaborative less-ismore filtering (CLiMF) [14] , and weighted approximately  ... 
dblp:conf/recsys/NagataniS17 fatcat:6uwjt4zxhnemlfs5tayf7mpycm

Personalized Video Recommendation Using Rich Contents from Videos [article]

Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou
2018 arXiv   pre-print
Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient.  ...  Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective  ...  Visual-CLiMF enhances VBPR by learning the approximate reciprocal rank instead of pairwise rank in the optimization.  ... 
arXiv:1612.06935v6 fatcat:vzbjtaosrvhprbg3ev6yj4fdki

Reliable graph-based collaborative ranking [article]

Bita Shams, Saman Haratizadeh
2018 arXiv   pre-print
To our knowledge, ReGRank is the first unified framework for neighborhood collaborative ranking that in addition to traditional user-based collaborative ranking, can also be adapted for preference-based  ...  This paper seeks to present a novel framework for reliable graph-based collaborative ranking, called ReGRank, that ranks items based on reliable recommendation paths that are in harmony with the semantics  ...  Recently, collaborative filtering algorithms are directed to learn the users' ranking over items.  ... 
arXiv:1811.01211v1 fatcat:zzufwnimkbeo5jtlganq3w5b3i
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