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View all- Said A(2025)On explaining recommendations with Large Language Models: a reviewFrontiers in Big Data10.3389/fdata.2024.15052847Online publication date: 27-Jan-2025
Recommender systems typically operate within a single domain, for example, recommending books based on users' reading habits. If such data is unavailable, it may be possible to make cross-domain recommendations and recommend books based on user ...
Session-based recommendations recently receive much attentions due to no available user data in many cases, e.g., users are not logged-in/tracked. Most session-based methods focus on exploring abundant historical records of anonymous users but ignoring ...
In the past decades, recommender systems have attracted much attention in both research and industry communities. Existing recommendation models mainly learn the underlying user preference from historical behavior data (typically in the forms of item IDs),...
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