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Robust contextual models for in-session personalization

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Yichao LuAuthors Info & Claims

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

Article No.: 2, Pages 1 - 5

https://doi.org/10.1145/3359555.3359558

Published: 20 September 2019 Publication History

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Abstract

Most online activity happens in the context of a session; to enable better user experience many online platforms aim to dynamically refine their recommendations as sessions progress. A popular approach is to continuously re-rank recommendations based on current session activity and past session logs. This motivates the 2019 ACM RecSys Challenge organised by Trivago. Using the session log dataset released by Trivago, the challenge aims to benchmark models for in-session re-ranking of hotel recommendations. In this paper we present our approach to this challenge where we first contextualize sessions in a global and local manner, and then train gradient boosting and deep learning models for re-ranking. Our team achieved 2^nd place out of over 570 teams, with less than 0.3% relative difference in Mean Reciprocal Rank from the 1^st place team. Code for our approach can be found here: https://github.com/layer6ai-labs/RecSys2019

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Alizadeh MTadayon MSakurai KAnada HJolfaei A(2021)A Secure Ticket-Based Authentication Mechanism for Proxy Mobile IPv6 Networks in Volunteer ComputingACM Transactions on Internet Technology10.1145/340718921:4(1-16)Online publication date: 22-Jul-2021
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Published In

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

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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Association for Computing Machinery

New York, NY, United States

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Published: 20 September 2019

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RecSys Challenge '19

RecSys Challenge '19: ACM Recommender Systems Challenge 2019 Workshop

September 20, 2019

Copenhagen, Denmark

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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
Alizadeh MTadayon MSakurai KAnada HJolfaei A(2021)A Secure Ticket-Based Authentication Mechanism for Proxy Mobile IPv6 Networks in Volunteer ComputingACM Transactions on Internet Technology10.1145/340718921:4(1-16)Online publication date: 22-Jul-2021
https://dl.acm.org/doi/10.1145/3407189
Lv ZQiao LKumar Singh AWang Q(2021)AI-empowered IoT Security for Smart CitiesACM Transactions on Internet Technology10.1145/340611521:4(1-21)Online publication date: 22-Jul-2021
https://dl.acm.org/doi/10.1145/3406115
Otoum SKantarci BMouftah H(2021)A Comparative Study of AI-Based Intrusion Detection Techniques in Critical InfrastructuresACM Transactions on Internet Technology10.1145/340609321:4(1-22)Online publication date: 22-Jul-2021
https://dl.acm.org/doi/10.1145/3406093
Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
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Du TWu KSpielberg AMatusik WZhu BSifakis E(2020)Functional optimization of fluidic devices with differentiable stokes flowACM Transactions on Graphics10.1145/3414685.341779539:6(1-15)Online publication date: 27-Nov-2020
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Barabasz BAnderson ASoodhalter KGregg D(2020)Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural NetworksACM Transactions on Mathematical Software10.1145/341238046:4(1-33)Online publication date: 7-Nov-2020
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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
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Recommendations

News Session-Based Recommendations using Deep Neural Networks

Self-Attentive Sequential Recommendation Models Enriched with More Features

Naïve filterbots for robust cold-start recommendations

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