A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA
release_7jxl7pap3bfyhkcjxjabs5irsu
by
Qian He,
Ke Wang
2024 Volume 15, Issue 1, p1-19
Abstract
Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.
In application/xml+jats
format
Archived Files and Locations
application/pdf
583.4 kB
file_tv2wdm4rt5db7bqjhmspumplji
|
www.igi-global.com (publisher) web.archive.org (webarchive) |
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar