research-article

A pipelined hybrid recommender system for ranking the items on the display

Authors:

Mun Yong YiAuthors Info & Claims

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

Article No.: 9, Pages 1 - 5

https://doi.org/10.1145/3359555.3359565

Published: 20 September 2019 Publication History

Abstract

In a session-based recommendation service, currently offered by many online companies including trivago, it is important to effectively incorporate user interactions into recommendations. However, a major challenge lies in the fact that both inter-session and intra-session contexts should be considered at the same time for recommendations to become effective. To address this issue, we propose a pipelined hybrid recommender system that considers the two contexts simultaneously via weighted summation of loss functions designed for the combination of a recurrent neural network (RNN) and a convolutional neural network (CNN). With the hybrid system, our team, OSI LAB, achieved the final score of 0.670167 and reached the 16th place in the RecSys Challenge 2019. Our source code is available from https://github.com/jhoon-oh/recsys2019challenge.

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Cited By

Bennawy Mel-Kafrawy P(2022)Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_59(514-523)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-3-031-09176-6_59
Adamczak JDeldjoo YMoghaddam FKnees PLeyson GMonreal P(2020)Session-based Hotel Recommendations DatasetACM Transactions on Intelligent Systems and Technology10.1145/341237912:1(1-20)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3412379

Index Terms

A pipelined hybrid recommender system for ranking the items on the display
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Other conferences

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

September 2019

49 pages

ISBN:9781450376679

DOI:10.1145/3359555

Program Chairs:
Peter Knees
TU Wien, Austria
,
Yashar Deldjoo
Polytechnic University of Bari, Italy
,
Farshad Bakhshandegan Moghaddam
Karlsruhe Institute ofTechnology, Germany
,
Jens Adamczak
trivago N.V., Germany
,
Gerard Leyson
trivago N.V., Germany
,
Philipp Monreal
trivago N.V., Germany

Copyright © 2019 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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

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ICT R&D program of MSIT/IITP

Conference

RecSys Challenge '19

RecSys Challenge '19: ACM Recommender Systems Challenge 2019 Workshop

September 20, 2019

Copenhagen, Denmark

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Overall Acceptance Rate 11 of 15 submissions, 73%

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Cited By

Bennawy Mel-Kafrawy P(2022)Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_59(514-523)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-3-031-09176-6_59
Adamczak JDeldjoo YMoghaddam FKnees PLeyson GMonreal P(2020)Session-based Hotel Recommendations DatasetACM Transactions on Intelligent Systems and Technology10.1145/341237912:1(1-20)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3412379

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