| Περίληψη: | Information load is growing daily as a result of the Internet’s, mobile devices’, and
e-phenomenal business’s expansion. This results in the creation of a system that
can filter and rank the pertinent data for consumers. Recommendation systems
have emerged as a solution to this problem, providing consumers with access to
individualized knowledge, goods, and services. While researchers have developed
various filtering strategies to improve user and system experiences, there is a need
to enhance the performance of cross-domain recommendation systems.
In this work, we propose a method for multi-domain recommendation systems
that leverages multiple datasets from various domains. By training our system on
these diverse datasets, we enable it to learn user preferences and behaviors across
different domains, resulting in more accurate recommendations. Our method incorporates cross-domain information, allowing the system to transcend traditional
domain boundaries and make relevant recommendations. Furthermore, we consider
contextual factors such as location, time, and device to further personalize the recommendations.
By incorporating these contributions, our method enhances the overall recommendation experience, providing users with a more holistic view and increasing the
relevance of recommendations. This advancement in cross-domain recommendation systems has the potential to improve user satisfaction and engagement in an
information-rich environment.
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