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Improving Co-Cluster Quality with Application to Product Recommendations

Published: 03 November 2014 Publication History

Abstract

Businesses store an ever increasing amount of historical customer sales data. Given the availability of such information, it is advantageous to analyze past sales, both for revealing dominant buying patterns, and for providing more targeted recommendations to clients. In this context, co-clustering has proved to be an important data-modeling primitive for revealing latent connections between two sets of entities, such as customers and products.
In this work, we introduce a new algorithm for co-clustering that is both scalable and highly resilient to noise. Our method is inspired by k-Means and agglomerative hierarchical clustering approaches: (i) first it searches for elementary co-clustering structures and (ii) then combines them into a better, more compact, solution. The algorithm is flexible as it does not require an explicit number of co-clusters as input, and is directly applicable on large data graphs. We apply our methodology on real sales data to analyze and visualize the connections between clients and products. We showcase a real deployment of the system, and how it has been used for driving a recommendation engine. Finally, we demonstrate that the new methodology can discover co-clusters of better quality and relevance than state-of-the-art co-clustering techniques.

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  • (2022)ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable RecommendationsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546756(246-256)Online publication date: 12-Sep-2022
  • (2021)Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning2021 29th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO54536.2021.9616223(1416-1420)Online publication date: 23-Aug-2021
  • (2021)A Federated Learning Approach for Privacy Protection in Context-Aware Recommender SystemsThe Computer Journal10.1093/comjnl/bxab02564:7(1016-1027)Online publication date: 30-Apr-2021
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cover image ACM Conferences
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
November 2014
2152 pages
ISBN:9781450325981
DOI:10.1145/2661829
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2014

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Author Tags

  1. cluster determination
  2. coclustering
  3. recommender systems

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2022)ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable RecommendationsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546756(246-256)Online publication date: 12-Sep-2022
  • (2021)Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning2021 29th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO54536.2021.9616223(1416-1420)Online publication date: 23-Aug-2021
  • (2021)A Federated Learning Approach for Privacy Protection in Context-Aware Recommender SystemsThe Computer Journal10.1093/comjnl/bxab02564:7(1016-1027)Online publication date: 30-Apr-2021
  • (2019)A personalized clustering-based approach using open linked data for search space reduction in recommender systemsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3349543(409-416)Online publication date: 29-Oct-2019
  • (2019)Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.282952131:7(1253-1266)Online publication date: 1-Jul-2019
  • (2017)Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.149(1033-1044)Online publication date: Apr-2017
  • (2016)Dynamic Recommendation: Disease Prediction and Prevention Using Recommender SystemInternational journal of basic science in medicine10.15171/ijbsm.2016.041:1(13-17)Online publication date: 29-Jun-2016
  • (2016)FastStep: Scalable Boolean Matrix DecompositionAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-31753-3_37(461-473)Online publication date: 12-Apr-2016
  • (2015)Alleviate sparsity problem using hybrid model based on spectral co-clustering and tensor factorization2015 5th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE.2015.7365843(285-289)Online publication date: Oct-2015

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