research-article

TFMAP: optimizing MAP for top-n context-aware recommendation

Authors:

Alexandros Karatzoglou,

Linas Baltrunas,

Nuria OliverAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 155 - 164

https://doi.org/10.1145/2348283.2348308

Published: 12 August 2012 Publication History

Abstract

In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work on context-aware recommendation has mainly focused on explicit feedback data, i.e., ratings. We propose TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information.

The optimization of MAP in a large data collection is computationally too complex to be tractable in practice. To address this computational bottleneck, we present a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency of TFMAP, and to ensure its scalability. We experimentally verify the effectiveness of the proposed fast learning algorithm, and demonstrate that TFMAP significantly outperforms state-of-the-art recommendation approaches.

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Cited By

Jallouli MLajmi SAmous I(2023)Contextualized Recommendation Model Based Socio-Environmental FactorsProceedings of the 27th International Database Engineered Applications Symposium10.1145/3589462.3589465(131-139)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3589462.3589465
Shivadekar SYesha YHalem MYang Z(2023)Multi-Variable Tensor Decomposition AnalyticsIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10281918(3885-3888)Online publication date: 16-Jul-2023
https://doi.org/10.1109/IGARSS52108.2023.10281918
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
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Index Terms

TFMAP: optimizing MAP for top-n context-aware recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 ACM.

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: 12 August 2012

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SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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Cited By

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https://dl.acm.org/doi/10.1145/3589462.3589465
Shivadekar SYesha YHalem MYang Z(2023)Multi-Variable Tensor Decomposition AnalyticsIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10281918(3885-3888)Online publication date: 16-Jul-2023
https://doi.org/10.1109/IGARSS52108.2023.10281918
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310470
Garigliotti DBalog KHose KBjerva J(2023)Recommending tasks based on search queries and missionsNatural Language Engineering10.1017/S1351324923000219(1-25)Online publication date: 17-May-2023
https://doi.org/10.1017/S1351324923000219
Jallouli MLajmi SAmous I(2022)REMOVE: REcommendation Model based on sOcio-enVironmental contExtMultimedia Tools and Applications10.1007/s11042-022-14239-382:16(24803-24840)Online publication date: 6-Dec-2022
https://doi.org/10.1007/s11042-022-14239-3
Dun MLi YYang HSun QLuan ZQian DZhou HMoreira JMueller FEtsion Y(2021)An optimized tensor completion library for multiple GPUsProceedings of the 35th ACM International Conference on Supercomputing10.1145/3447818.3460692(417-430)Online publication date: 3-Jun-2021
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Fan YTu ZLi TCao HXia TLi YChen XZhang L(2021)Understanding the Long-term Dynamics of Mobile App Usage Context via Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3110141(1-1)Online publication date: 2021
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Cao BZhao JLv ZYang P(2021)Diversified Personalized Recommendation Optimization Based on Mobile DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304090922:4(2133-2139)Online publication date: Apr-2021
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Dun MLi YSun QYang HLi WLuan ZGan LYang GQian D(2021)Towards efficient canonical polyadic decomposition on sunway many-core processorInformation Sciences10.1016/j.ins.2020.11.013549(221-248)Online publication date: Mar-2021
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Twardowski BZawistowski PZaborowski S(2021)Metric Learning for Session-Based RecommendationsAdvances in Information Retrieval10.1007/978-3-030-72113-8_43(650-665)Online publication date: 28-Mar-2021
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