A session-based recommender that uses a large language model to infer multiple user intents from a GNN-selected candidate set and aligns them with the GNN's structural representation, improving ranking metrics on Beauty and ML-1M.
Hybrid Session-based News Recommendation using Recurrent Neural Networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of modeling the sequence of session clicks with RNNs and leveraging side information about users and articles, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms.
citation-role summary
background 1
citation-polarity summary
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation
A session-based recommender that uses a large language model to infer multiple user intents from a GNN-selected candidate set and aligns them with the GNN's structural representation, improving ranking metrics on Beauty and ML-1M.