Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term
Popis výsledku v původním jazyce
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'Hondt'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.
Název v anglickém jazyce
Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term
Popis výsledku anglicky
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'Hondt'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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexibilní modely pro hledání známé scény v rozsáhlých kolekcích videa</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
ACM International Conference Proceeding Series
ISBN
978-1-4503-9611-0
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
280-285
Název nakladatele
ACM
Místo vydání
New York, NY, USA
Místo konání akce
Tianjin, China
Datum konání akce
18. 3. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—