Offline evaluation of the serendipity in recommendation systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00363087" target="_blank" >RIV/68407700:21240/22:00363087 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10000782" target="_blank" >https://ieeexplore.ieee.org/document/10000782</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CSIT56902.2022.10000782" target="_blank" >10.1109/CSIT56902.2022.10000782</a>
Alternative languages
Result language
angličtina
Original language name
Offline evaluation of the serendipity in recommendation systems
Original language description
Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder (EASE). We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE 17th International Conference on Computer Science and Information Technologies
ISBN
979-8-3503-3431-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
597-601
Publisher name
IEEE
Place of publication
Dortmund
Event location
Lvov
Event date
Nov 10, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000927642900140