Presenting our work accepted by @cikm2023! Curious how a sequential recommender captures users' shopping intents from stochastic shopping behaviors? We present G-STO, a graph regularized stochastic Transformer.
Released at @AmazonScience#Transformers#RecommenderSystem#ai
Our work accepted at KDD’s MLG workshop @kdd_news 🎉 Our work learns customers’ shopping journey into a graph representation while distilling a prior knowledge graph for explicit personalizations. Released at @AmazonScience https://t.co/gxYLFhtgYQ #KDD2023#ai#RecommenderSystem
Our work accepted at KDD’s MLG workshop @kdd_news 🎉 Our work learns customers’ shopping journey into a graph representation while distilling a prior knowledge graph for explicit personalizations. Released at @AmazonScience https://t.co/gxYLFhtgYQ #KDD2023#ai#RecommenderSystem
📢 Just released: Paper schedules for #KDD2023! Plan ahead and don't miss a beat.
🔍Research track: https://t.co/UF510ejMob
📈ADS Track: https://t.co/DwhKWTA0Im
Dive into the conference hype and get a sneak peek by watching their promo videos on YouTube: https://t.co/shs87MK9N6
Announcing our accepted paper by #KDD2023! Our #NLP based #RecommenderSystem , leverages text representations to actively capture correlation between user preference and item features
Paper: https://t.co/uemgqxVE0S
Code: https://t.co/0Vy84vDB3W
#AI#DeepLearning#MachineLearning
Announcing our accepted paper by #KDD2023! Our #NLP based #RecommenderSystem , leverages text representations to actively capture correlation between user preference and item features
Paper: https://t.co/uemgqxVE0S
Code: https://t.co/0Vy84vDB3W
#AI#DeepLearning#MachineLearning