abstract

Interactive View Recommendation

Author:

Xiaozhong ZhangAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 2849 - 2851

https://doi.org/10.1145/3318464.3384407

Published: 31 May 2020 Publication History

Abstract

Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones, along with their tunable parameters, which represent the user's intention during an exploration, is a challenge for both expert and non-expert users. This paper presents an attempt towards interactive view recommendation that automatically discovers the utility function composition during an exploration that best matches the user's intentions and exploration task.

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Cited By

Gong YZhu ZGalhotra SFernandez R(2023)Ver: View Discovery in the Wild2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00045(503-516)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00045
Da Col SCiucanu RSoare MBouarour NAmer-Yahia SDemartini GZuccon GCulpepper JHuang ZTong H(2021)DashBotProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481968(4696-4700)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481968
Qiao KWu XZhan THuang FZhou J(2021)Research on the interconnection and rapid deployment of smart energy service platforms under multi-factor game balanceMATEC Web of Conferences10.1051/matecconf/202133605029336(05029)Online publication date: 15-Feb-2021
https://doi.org/10.1051/matecconf/202133605029

Index Terms

Interactive View Recommendation

Recommendations

Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Attention-driven Factor Model for Explainable Personalized Recommendation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Latent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item features ...
Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based ...

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2020

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SIGMOD/PODS '20

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SIGMOD

SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Gong YZhu ZGalhotra SFernandez R(2023)Ver: View Discovery in the Wild2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00045(503-516)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00045
Da Col SCiucanu RSoare MBouarour NAmer-Yahia SDemartini GZuccon GCulpepper JHuang ZTong H(2021)DashBotProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481968(4696-4700)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481968
Qiao KWu XZhan THuang FZhou J(2021)Research on the interconnection and rapid deployment of smart energy service platforms under multi-factor game balanceMATEC Web of Conferences10.1051/matecconf/202133605029336(05029)Online publication date: 15-Feb-2021
https://doi.org/10.1051/matecconf/202133605029

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