Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448376" target="_blank" >RIV/00216208:11320/22:10448376 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3532213.3532255" target="_blank" >https://doi.org/10.1145/3532213.3532255</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3532213.3532255" target="_blank" >10.1145/3532213.3532255</a>

Result language

angličtina

Original language name

Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term

Original language description

This paper focuses on the recommendation problem from the perspective of hierarchical heterogeneous portfolios. These portfolios consist of several base recommenders. Each of them processes different subset of available data and achieves the best performance under different circumstances. By running base recommenders in parallel and employing a suitable aggregation of their results (i.e. ensemble approach) considerable performance gains can be achieved. The main contribution of this paper is the proposal of a hierarchical ensemble approach to the recommendation problem and its utilization in the case of repeated recommendations. We extend flat portfolios to hierarchical ones with two levels of aggregation. For the aggregation of base recommenders, we experimented with Thompson sampling multi-armed bandits algorithm and a modified version of D&apos;Hondt&apos;s mandates allocation algorithm. As for the hierarchical aggregation, we implemented a modified version of D21-Janecek mandate allocation algorithm, which allows us to incorporate implicit negative feedback as well. Experiments were performed on real-world data from the domain of e-commerce. Hierarchical portfolios outperformed both flat portfolios as well as individual base recommenders w.r.t. click-through rate.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexible models for known-item search in large video collections</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ACM International Conference Proceeding Series

ISBN

978-1-4503-9611-0

ISSN

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e-ISSN

—

Number of pages

6

Pages from-to

280-285

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Tianjin, China

Event date

Mar 18, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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