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A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation [article]

Mohammad Aliannejadi and Dimitrios Rafailidis and Fabio Crestani
2019 arXiv   pre-print
Recommending points of interest (POIs) plays a key role in satisfying users' needs in LBSNs.  ...  Experiments on real-world datasets show that our proposed time-sensitive collaborative ranking model beats state-of-the-art POI recommendation methods.  ...  ACKNOWLEDGEMENT This research was partially funded by the RelMobIR project of the Swiss National Science Foundation (SNSF).  ... 
arXiv:1909.07131v1 fatcat:c7ge4utjkvfull2d37s4bvirgu

Point of Interest Recommendation engine

2020 International Journal of Recent Trends in Engineering and Research  
While recent work has explored the thought of adopting a collaborative ranking (CR) for recommendations, few attempts are made to include time-based information for POI recommendations using CR.  ...  Real-world dataset experiments show that our proposed time-sensitive collaborative ranking model beats the state-of -the-art POI recommendation methods.  ...  a typically time-sensitive regularized, taking into account the variation in the prevalence over time of consumer behaviours and locations. • We are proposing a new, two-phase CR-based POI recommendation  ... 
doi:10.23883/ijrter.2020.6013.5onhy fatcat:kobcu2nesbgwjftzmd57zeukse

A Survey of Point-of-interest Recommendation in Location-based Social Networks [article]

Shenglin Zhao, Irwin King, Michael R. Lyu
2016 arXiv   pre-print
Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs).  ...  Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods.  ...  There are two ways to construct a POI recommendation system: the fused model and the joint model.  ... 
arXiv:1607.00647v1 fatcat:prstldhamremzpkmbyf5oh6jom

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Shameem A. Puthiya Parambath, Sanjay Chawla
2020 Data mining and knowledge discovery  
Recommender systems are widely used in online platforms for easy exploration of personalized content.  ...  Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem.  ...  To view a copy of this licence, visit  ... 
doi:10.1007/s10618-020-00708-6 fatcat:xhor425vmfe5bljgggn5bexpxi

Accuracy-diversity trade-off in recommender systems via graph convolutions

Elvin Isufi, Matteo Pocchiari, Alan Hanjalic
2021 Information Processing & Management  
The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning.  ...  Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender systems.  ...  Two GCNNs are run over two graphs, a point-of-interest graph and a social relationship graph to identify these points-of-interest for a user.  ... 
doi:10.1016/j.ipm.2020.102459 fatcat:qsirbchbqfhrbdx3b52fgdf7nq

Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches

Dung D. Le, Hady Lauw
2021 The Journal of Artificial Intelligence Research  
However, for the recommendation retrieval phase, naively scanning a large number of items to identify the few most relevant ones may inhibit truly real-time applications.  ...  A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval.  ...  NSW (Malkov & Yashunin, 2019) is proposed to take advantage of the Delaunay Graph, the NSWN, and the Relative Neighborhood Graphs, enabling multi-scale hopping on different layers of the graph.  ... 
doi:10.1613/jair.1.12403 fatcat:fpum5xffmbhclme3hdmmbs34uy

Preference Networks: Probabilistic Models for Recommendation Systems [article]

Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
2014 arXiv   pre-print
We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation.  ...  The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field.  ...  Sec. 2), where we treat each entry r ui in M as a random variable, and thus ideally we would be interested in a single joint model over KM variables for both the learning phase and the prediction/recommendation  ... 
arXiv:1407.5764v1 fatcat:t4jvv7lqa5blli5omhtxy5jmyq

Discrete Factorization Machines for Fast Feature-based Recommendation

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.  ...  User and item features of side information are crucial for accurate recommendation.  ...  As practical recommender systems typically recommend a list of items for a user, we rank the testing items of a user and evaluate the ranked list with Normalized Discounted Cumulative Gain (NDCG), which  ... 
doi:10.24963/ijcai.2018/479 dblp:conf/ijcai/Liu0FNLZ18 fatcat:o4i62ez52zdqlepreyzuo7suq4

A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector

William Lemus Leiva, Meng-Lin Li, Chieh-Yuan Tsai
2021 Applied Sciences  
To solve the above drawback, this study proposes a two-phase deep learning-based recommender system.  ...  Firstly, a sentiment predictor of textual reviews is created, serving as the quality inspector that cleans and improves the input for a recommender.  ...  Final ranking of models through sensitivity analysis of the three indicators' weight distribution. 4. 3 . 3 Deep Learning-Based Product Recommender 4.3.1.  ... 
doi:10.3390/app11209667 fatcat:ve53vogcnrgkrhes6ukuomqxuu

Map-Based Recommendation of Hyperlinked Document Collections [chapter]

Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Krzysztof Ciesielski, Michał Dramiński, Dariusz Czerski
2006 Lecture Notes in Computer Science  
The increasing number of documents returned by search engines for typical requests makes it necessary to look for new methods of representation of the search results.  ...  PageRank) in order to build visual recommender system.  ...  Our starting point was widely-known Kohonen's Self-Organizing Map principle [19] , which is an unsupervised learning neural network model, consisted of regular, 2D grid of neurons.  ... 
doi:10.1007/11823865_1 fatcat:5nnulalvpjf77kfafsgvvk2r44

Discrete Collaborative Filtering

Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat-Seng Chua
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a  ...  We devise a computationally efficient algorithm with a rigorous convergence proof of DCF.  ...  BCCF: This is a two-stage Binary Code learning method for Collaborative Filtering [33] .  ... 
doi:10.1145/2911451.2911502 dblp:conf/sigir/ZhangSLHLC16 fatcat:47j23aqxzve5xfxsmabsf7tuqu

Graph Meta Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
2021 arXiv   pre-print
diversity for recommendations.  ...  To tackle the above challenges, we propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm.  ...  ACKNOWLEDGMENTS We thank the reviewers for their valuable comments.  ... 
arXiv:2110.03969v1 fatcat:w4mrfiyvabgsvhcu7jzototitu

Coupled Variational Recurrent Collaborative Filtering [article]

Qingquan Song, Shiyu Chang, Xia Hu
2019 arXiv   pre-print
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner.  ...  To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation  ...  ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their helpful comments.  ... 
arXiv:1906.04386v1 fatcat:ldsxz2xb2ffu7ju4mid56suxye

Urban point-of-interest recommendation by mining user check-in behaviors

Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo, Vincent S. Tseng
2012 Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12  
In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention.  ...  Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building.  ...  RMF recommender first maps users and spots to a joint latent factor space. RMF recommender exploits regularized Singular Value Decomposition (SVD) model to predict the ratings of users to spots.  ... 
doi:10.1145/2346496.2346507 dblp:conf/kdd/YingLKT12 fatcat:qkpdl3kzwvdh5e3xxmyg5mgtpm

Reporting Results in High Energy Physics Publications: a Manifesto

Pietro Vischia
2020 Reviews in Physics  
In this manuscript I advocate for an increase in the information shared by the Collaborations, and try to define a minimum standard for acceptable level of information when reporting the results of statistical  ...  Analysis methods evolved to account for the increased complexity of the combination of particles required in each collision event (final states) and for the need of squeezing every last bit of sensitivity  ...  Acknowledgements I wish to thank the ATLAS and CMS Collaborations for being an endless source of inspiration, the CMS Collaboration for having me among its members since exactly ten years, four of which  ... 
doi:10.1016/j.revip.2020.100046 fatcat:gen7owoifzhbhgxk7olqagfxsq
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