SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10494537" target="_blank" >RIV/00216208:11320/24:10494537 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3626772.3657863" target="_blank" >https://doi.org/10.1145/3626772.3657863</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3626772.3657863" target="_blank" >10.1145/3626772.3657863</a>

Result language

angličtina

Original language name

SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores

Original language description

Recommender systems (RS) rely on interaction data between users and items to generate effective results. Historically, RS aimed to deliver the most consistent (i.e., accurate) items to the trained user profiles. However, the attention towards additional ( beyond-accuracy) quality criteria has increased tremendously in recent years. Both the research and applied models are being optimized for diversity, novelty, or fairness, to name a few. Naturally, the proper functioning of such optimization methods depends on the knowledge of users&apos; propensities towards interacting with recommendations having certain quality criteria. However, so far, no dataset that captures such propensities exists. To bridge this research gap, we present SM-RS (single-objective + multi-objective recommendations dataset) that links users&apos; selfdeclared propensity toward relevance, novelty, and diversity criteria with impressions and corresponding item selections. After presenting the dataset&apos;s collection procedure and basic statistics, we propose three tasks that are rarely available to conduct using existing RS datasets: impressions-aware click prediction, users&apos; propensity scores prediction, and construction of recommendations proportional to the users&apos; propensity scores. For each task, we also provide detailed evaluation procedures and competitive baselines. The dataset is available at https://osf.io/hkzje/.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

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/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

PROCEEDINGS OF THE 47TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2024

ISBN

979-8-4007-0431-4

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

988-995

Publisher name

ASSOC COMPUTING MACHINERY

Place of publication

NEW YORK

Event location

Washington

Event date

Jul 14, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001273410001007