GCZRec: Generative Collaborative Zero-Shot Framework for Cold Start News Recommendation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254338" target="_blank" >RIV/61989100:27240/24:10254338 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414986" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414986</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3359053" target="_blank" >10.1109/ACCESS.2024.3359053</a>
Alternative languages
Result language
angličtina
Original language name
GCZRec: Generative Collaborative Zero-Shot Framework for Cold Start News Recommendation
Original language description
The aim of personalized news recommendation is to suggest news stories to the users that are most interesting for them. To improve the user experience, it is important that these news items are not only relevant to the user but also get recommended to them as soon as they are available. The inability of traditional collaborative filtering approach to recommend such cold start items has led to techniques that incorporate latent features of items in order to make cold start recommendations such as content based filtering and deep neural network-based approaches. However, these existing techniques do not make use of any collaborative information between users and items as well as latent features at the same time and thus fail to provide any serendipity which is an important aspect of any recommender system. Moreover, these underlying collaborative signals between users and items are crucial to improving the overall quality of recommender systems and can also be utilized to make cold start recommendations. In this paper, we propose the Generative Collaborative Zero-Shot Recommender System framework (GCZRec) which makes use of both the latent user and item features as well as the underlying collaborative information to generate both warm start and cold start recommendations. We evaluate our framework for news recommendation task given cold start and warm start cases for both users and news items. We also discuss that our model can be plugged in and used as preprocessing to improve the performance of an existing recommender system.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
16610-16620
UT code for WoS article
001161858700001
EID of the result in the Scopus database
2-s2.0-85183612709