research-article

Query-driven context aware recommendation

Authors:

Bamshad Mobasher,

Robin BurkeAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 9 - 16

https://doi.org/10.1145/2507157.2507187

Published: 12 October 2013 Publication History

Abstract

Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly.

We propose a unified probabilistic model that integrates user profiles, item representations, and contextual information. The resulting recommendation framework computes the conditional probability of each item given the user profile and the additional context. These probabilities are used as recommendation scores for ranking items. Our model is an extension of the Latent Dirichlet Allocation (LDA) model that provides the capability for joint modeling of users, items, and the meta-data associated with contexts. Each user profile is modeled as a mixture of the latent topics. The discovered latent topics enable our system to handle missing data in item features. We demonstrate the application of our framework for article and music recommendation. In the latter case, the set of popular tags from social tagging Web sites are used for context descriptions. Our evaluation results show that considering context can help improve the quality of recommendations.

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Cited By

Eberhard LPopova KWalk SHelic D(2024)Computing recommendations from free-form textExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121268236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121268
Teppalwar VSahoo KJaiswal RMunot M(2024)A Survey on Personalized Movie Recommendation System Using Machine LearningSmart Trends in Computing and Communications10.1007/978-981-97-1320-2_25(305-314)Online publication date: 14-Jun-2024
https://doi.org/10.1007/978-981-97-1320-2_25
Mysore SMccallum AZamani H(2023)Large Language Model Augmented Narrative Driven RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608829(777-783)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608829
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Index Terms

Query-driven 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

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

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Publication History

Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Eberhard LPopova KWalk SHelic D(2024)Computing recommendations from free-form textExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121268236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121268
Teppalwar VSahoo KJaiswal RMunot M(2024)A Survey on Personalized Movie Recommendation System Using Machine LearningSmart Trends in Computing and Communications10.1007/978-981-97-1320-2_25(305-314)Online publication date: 14-Jun-2024
https://doi.org/10.1007/978-981-97-1320-2_25
Mysore SMccallum AZamani H(2023)Large Language Model Augmented Narrative Driven RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608829(777-783)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608829
Shah CWhite R(2021)Task Intelligence for Search and RecommendationSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S01103ED1V01Y202105ICR07413:3(1-160)Online publication date: 9-Jun-2021
https://doi.org/10.2200/S01103ED1V01Y202105ICR074
Zhang CKiseleva JJauhar SWhite R(2021)Grounded Task Prioritization with Context-Aware Sequential RankingACM Transactions on Information Systems10.1145/348686140:4(1-28)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3486861
Sarrafzadeh BJauhar SGamon MLank EWhite R(2021)Characterizing Stage-aware Writing Assistance for Collaborative Document AuthoringProceedings of the ACM on Human-Computer Interaction10.1145/34341804:CSCW3(1-29)Online publication date: 5-Jan-2021
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Kumar GJerbi HO’Mahony M(2021)A sequence-based and context modelling framework for recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114665175:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114665
Adomavicius GBauman KTuzhilin AUnger M(2021)Context-Aware Recommender Systems: From Foundations to Recent DevelopmentsRecommender Systems Handbook10.1007/978-1-0716-2197-4_6(211-250)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_6
Dinh LKarmakar GKamruzzaman J(2020)A survey on context awareness in big data analytics for business applicationsKnowledge and Information Systems10.1007/s10115-020-01462-3Online publication date: 21-Apr-2020
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Eberhard LWalk SPosch LHelic DFu WPan SBrdiczka OChau PCalvary G(2019)Evaluating narrative-driven movie recommendations on RedditProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302287(1-11)Online publication date: 17-Mar-2019
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