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Rethinking cross-domain sequential recommendation under open-world assumptions
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
Slmrec: empowering small language models for sequential recommendation
Sequential Recommendation (SR) task involves predicting the next item a user is likely to
interact with, given their past interactions. The SR models examine the sequence of a user's …
interact with, given their past interactions. The SR models examine the sequence of a user's …
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
In most practical applications such as recommendation systems, display advertising, and so
forth, the collected data often contains missing values and those missing values are …
forth, the collected data often contains missing values and those missing values are …
Information maximization via variational autoencoders for cross-domain recommendation
Cross-Domain Sequential Recommendation (CDSR) methods aim to address the data
sparsity and cold-start problems present in Single-Domain Sequential Recommendation …
sparsity and cold-start problems present in Single-Domain Sequential Recommendation …
InstructAgent: Building User Controllable Recommender via LLM Agent
Traditional recommender systems usually take the user-platform paradigm, where users are
directly exposed under the control of the platform's recommendation algorithms. However …
directly exposed under the control of the platform's recommendation algorithms. However …
Cross-Domain Sequential Recommendation via Neural Process
Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user
interest modeling, which aims at utilizing a single model to predict the next items for different …
interest modeling, which aims at utilizing a single model to predict the next items for different …