Filters








230 Hits in 3.0 sec

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.  ...  In this paper, we propose a new ranking-oriented CF algorithm, called ListCF.  ...  LISTWISE COLLABORATIVE FILTER-ING In this section, we propose ListCF, a listwise rankingoriented collaborative filtering (CF) algorithm based on the memory-based CF framework.  ... 
doi:10.1145/2766462.2767693 dblp:conf/sigir/HuangWLMCV15 fatcat:3j55zjitzbfa5nrumqwb2zbwsy

Listwise Collaborative Filtering with High-Rating-Based Similarity and Simple Missing Value Estimation

Yoshiki TSUCHIYA, Hajime NOBUHARA
2019 Journal of Japan Society for Fuzzy Theory and Intelligent Informatics  
factor of up to 50) and improve the normalized discounted cumulative gain value by up to 0.02 compared with ListCF, a well-known listwise collaborative filtering algorithm.  ...  filtering using a simple missing value estimation process.  ...  Ranking-oriented CF algorithms can be further divided into two types: pairwise [3, 5] and listwise [1] approaches.  ... 
doi:10.3156/jsoft.31.1_501 fatcat:jchw3ii3mvg6dbldkihzwyhbce

Probabilistic latent preference analysis for collaborative filtering

Nathan N. Liu, Min Zhao, Qiang Yang
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
A central goal of collaborative filtering (CF) is to rank items by their utilities with respect to individual users in order to make personalized recommendations.  ...  An EM algorithm for fitting the corresponding latent class model as well as a method for predicting the optimal ranking are described.  ...  CONCLUSIONS AND FUTURE WORK In this paper, we propose the probabilistic latent preference analysis model for ranking-oriented collaborative filtering.  ... 
doi:10.1145/1645953.1646050 dblp:conf/cikm/LiuZY09 fatcat:5bj4u25tobf27ph7cokzomcvzy

Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering [article]

Chang Liu, Han Yu, Boyang Li, Zhiqi Shen, Zhanning Gao, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao
2021 arXiv   pre-print
PRISM calculates the probability of a label being clean, and filters out potentially noisy samples.  ...  In this paper, we bridge this important gap by proposing Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML.  ...  Wang, “Orientation invariant feature embedding and spatial [50] G. Zheng, A. H. Awadallah, and S.  ... 
arXiv:2108.01431v2 fatcat:vz5d4iqshvds7iaqohl7js4ieq

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
top ranked items a user might like by leveraging implicit user-item interaction data.  ...  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  ...  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

A Unified Energy-based Framework for Learning to Rank

Yi Fang, Mengwen Liu
2016 Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval - ICTIR '16  
In this paper, we introduce a unified view of Learning to Rank that integrates various L2R approaches in an energy-based ranking framework.  ...  The proposed framework yields new insights into learning to rank. First, we show how various existing L2R models (pointwise, pairwise, and listwise) can be cast in the energy-based framework.  ...  INTRODUCTION Ranking is the central problem in many IR tasks including document retrieval, entity search, question answering, meta-search, collaborative filtering, online advertisement, and so on.  ... 
doi:10.1145/2970398.2970416 dblp:conf/ictir/FangL16 fatcat:vd6jtak7xraizemsymy5stk5xq

A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems [article]

Hyunsung Lee, Yeongjae Jang, Jaekwang Kim, Honguk Woo
2020 arXiv   pre-print
A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores.  ...  In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance by employing the differentiable relaxation of ranking metrics.  ...  Listwise Collaborative Filtering (Huang et al. 2015) addresses the misalignment issue between cost and objective on K-Nearest neighbors recommenders.  ... 
arXiv:2008.13141v4 fatcat:bgxf3itixjbypblycbng3nvnuq

Density-Ratio Based Personalised Ranking from Implicit Feedback [article]

Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh
2021 arXiv   pre-print
The key idea is to estimate the ranking of items in a pointwise manner; we first reformulate the conventional pointwise approach based on density ratio estimation and then incorporate the essence of ranking-oriented  ...  By contrast, a pointwise approach does not directly solve a ranking problem, and is therefore inferior to a pairwise counterpart in top-K ranking tasks; however, it is generally advantageous in regards  ...  The listwise approach is another direction of ranking-oriented methods [22, 46] .  ... 
arXiv:2101.07481v1 fatcat:kn5i7eihunbqfezxc3lnyoru6m

Regression and Learning to Rank Aggregation for User Engagement Evaluation [article]

Hamed Zamani, Azadeh Shakery, Pooya Moradi
2015 arXiv   pre-print
In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites  ...  The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.  ...  User oriented tweet ranking: Discovery and Data Mining, KDD ’02, pages 133–142, A filtering approach to microblogs.  ... 
arXiv:1501.07467v1 fatcat:bomiyn6wdrevjchkdgq37hyzz4

Conference Paper Recommendation for Academic Conferences

Shuchen Li, Peter Brusilovsky, Sen Su, Xiang Cheng
2018 IEEE Access  
Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user's preference towards a paper based on the extracted features  ...  While most of the related existing methods depend on the content-based filtering, we propose a unified recommendation method which exploits both the contents and the authorship information of the papers  ...  [9] presented a book recommender system which applies a ranking-oriented collaborative filtering method that exploits the data from users' access logs for recommendation.  ... 
doi:10.1109/access.2018.2817497 fatcat:khbmkdl7lvdl7ivtk6teany3gm

A latent pairwise preference learning approach for recommendation from implicit feedback

Yi Fang, Luo Si
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Furthermore, it is often more suitable for many recommender systems to address a ranking problem than a rating predicting problem.  ...  This paper proposes a latent pairwise preference learning (LPPL) approach for recommendation with implicit feedback.  ...  Another baseline includes wAMAN proposed in [6] for weighting implicit feedback, which is a collaborative filtering based approach.  ... 
doi:10.1145/2396761.2398693 dblp:conf/cikm/FangS12 fatcat:ypcswqpnavfvjneqiwhj25deia

A dual-perspective latent factor model for group-aware social event recommendation

Yogesh Jhamb, Yi Fang
2017 Information Processing & Management  
perspective (e.g., topics of interest) and another from the event-oriented perspective (e.g., event planning and organization).  ...  ., movies and books), a large majority of EBSN users join groups unified by a common interest, and events are organized by groups.  ...  Moreover, we will investigate learning to rank based recommendation ( Belem, Martins, Almeida, & Goncalves, 2014 ) such as the listwise recommendation approach by taking a ranked list of items as a training  ... 
doi:10.1016/j.ipm.2017.01.001 fatcat:l6tpel7d5vg6hih4tgmpfk7uyq

Learning to Recommend Accurate and Diverse Items

Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, Hui Xiong
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems.  ...  In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as groundtruth for each user.  ...  They firstly use a standard ranking approach to maximize the accuracy of recommendations.  ... 
doi:10.1145/3038912.3052585 dblp:conf/www/ChengWMSX17 fatcat:7rlsg54qcjdknirmn3gmuu52vi

Mining mood-specific movie similarity with matrix factorization for context-aware recommendation

Yue Shi, Martha Larson, Alan Hanjalic
2010 Proceedings of the Workshop on Context-Aware Movie Recommendation - CAMRa '10  
Recommendations should also usually strive to satisfy a specific purpose.  ...  Our measure is further exploited by a joint matrix factorization model for recommendation.  ...  Collaborative Filtering A comprehensive survey of collaborative filtering approaches can be found in [1] [10] .  ... 
doi:10.1145/1869652.1869658 fatcat:gy7zd3m5ijdwzbtvzkatffjg2y

Neural Semantic Personalized Ranking for item cold-start recommendation

Travis Ebesu, Yi Fang
2017 Information retrieval (Boston)  
To address the above challenges, we propose a probabilistic modeling approach called Neural Semantic Personalized Ranking (NSPR) to unify the strengths of deep neural network and pairwise learning.  ...  Specifically, NSPR tightly couples a latent factor model with a deep neural network to learn a robust feature representation from both implicit feedback and item content, consequently allowing our model  ...  A popular and effective approach to recommendations is collaborative filtering (CF), which focuses on finding users with similar interests and recommending items favored by the like-minded (Koren 2010  ... 
doi:10.1007/s10791-017-9295-9 fatcat:t5hercdtcvblphotbrrsp4z7my
« Previous Showing results 1 — 15 out of 230 results