Transformers4Rec: A flexible library for Sequential and Session-based recommendation

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Transformers4Rec: A flexible library for Sequential and Session-based recommendation

Transformers4Rec: A flexible library for Sequential and Session-based recommendation

“Recommender systems help users to find relevant content, products, media and much more in online services. They also help such services to connect their long-tailed (unpopular) items to the right people, to keep their users engaged and increase conversion.

Traditional recommendation algorithms, e.g. collaborative filtering, usually ignore the temporal dynamics and the sequence of interactions when trying to model user behaviour. But users’ preferences do change over time. Sequential recommendation algorithms are able to capture sequential patterns in users browsing might help to anticipate the next user interests for better recommendation. For example, users getting started into a new hobby like cooking or cycling might explore products for beginners, and move to more advanced products as they progress over time. They might also completely move to another topic of interest, so that recommending items related to their long past preferences would become irrelevant.

A special case of sequential-recommendation is the session-based recommendation task, where you have only access to the short sequence of interactions within the current session. This is very common in online services like e-commerce, news and media portals, where the user might be brand new or prefer to browse anonymously (and due to GDPR compliance no cookies are collected). This task is also relevant for scenarios where users’ interests change a lot over time depending on the user context or intent, so leveraging the current session interactions is more promising than old interactions to provide relevant recommendations…”

Source: medium.com/nvidia-merlin/transformers4rec-4523cc7d8fa8

September 29, 2021
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