research-article

Top-N Recommendation on Graphs

Authors:

Qiang ChengAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2101 - 2106

https://doi.org/10.1145/2983323.2983649

Published: 24 October 2016 Publication History

Abstract

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.

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Cited By

Zhang CChu XMa LZhu YWang YWang JZhao JZhang ARangwala H(2022)M3Care: Learning with Missing Modalities in Multimodal Healthcare DataProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539388(2418-2428)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539388
Kaya TKaleli C(2022)A novel top-n recommendation method for multi-criteria collaborative filteringExpert Systems with Applications10.1016/j.eswa.2022.116695198(116695)Online publication date: Jul-2022
https://doi.org/10.1016/j.eswa.2022.116695
Liu YMa HJiang YLi Z(2022)Learning to recommend via random walk with profile of loan and lender in P2P lendingExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114763174:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.114763
Show More Cited By

Index Terms

Top-N Recommendation on Graphs

Recommendations

Local Item-Item Models For Top-N Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in ...
Convex AUC optimization for top-N recommendation with implicit feedback
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

In this paper, an effective collaborative filtering algorithm for top-N item recommendation with implicit feedback is proposed. The task of top-N item recommendation is to predict a ranking of items (movies, books, songs, or products in general) that ...
Personalized hybrid recommendation for group of users

Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single ...

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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U.S. National Science Foundation

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CIKM'16

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Zhang CChu XMa LZhu YWang YWang JZhao JZhang ARangwala H(2022)M3Care: Learning with Missing Modalities in Multimodal Healthcare DataProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539388(2418-2428)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539388
Kaya TKaleli C(2022)A novel top-n recommendation method for multi-criteria collaborative filteringExpert Systems with Applications10.1016/j.eswa.2022.116695198(116695)Online publication date: Jul-2022
https://doi.org/10.1016/j.eswa.2022.116695
Liu YMa HJiang YLi Z(2022)Learning to recommend via random walk with profile of loan and lender in P2P lendingExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114763174:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.114763
Chen YWang YZhao XZou JRijke M(2020)Block-Aware Item Similarity Models for Top-N RecommendationACM Transactions on Information Systems10.1145/341175438:4(1-26)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3411754
Nikolakopoulos AKarypis G(2020)Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random WalksACM Transactions on Knowledge Discovery from Data10.1145/340624114:6(1-26)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406241
Nikolakopoulos ABerberidis DKarypis GGiannakis GBogers TSaid ABrusilovsky PTikk D(2019)Personalized diffusions for top-n recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346985(260-268)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3346985
Nikolakopoulos AKarypis GCulpepper JMoffat ABennett PLerman K(2019)RecWalkProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291016(150-158)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3291016
Lin ZFeng LKwoh CXu C(2019)Fast Top-N Personalized Recommendation on Item Graph2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006387(3903-3908)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006387
Bai HChen ZLyu MKing IXu ZCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Neural Relational Topic Models for Scientific Article AnalysisProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271696(27-36)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271696
Zhang YXiong YKong XZhu YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Learning Node Embeddings in Interaction GraphsProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132918(397-406)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132918
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