X-Cross: Dynamic Integration of Language Models for Cross-Domain
  Sequential Recommendation
As new products are emerging daily, recommendation systems are required to
quickly adapt to possible new domains without needing extensive retraining.
This work presents ``X-Cross'' -- a novel cross-domain
sequential-recommendation model that recommends products in new domains by
integrating several domain-specific language models; each model is fine-tuned
with low-rank adapters (LoRA). Given a recommendation prompt, operating layer
by layer, X-Cross dynamically refines the representation of each source
language model by integrating knowledge from all other models. These refined
representations are propagated from one layer to the next, leveraging the
activations from each domain adapter to ensure domain-specific nuances are
preserved while enabling adaptability across domains. Using Amazon datasets for
sequential recommendation, X-Cross achieves performance comparable to a model
that is fine-tuned with LoRA, while using only 25% of the additional
parameters. In cross-domain tasks, such as adapting from Toys domain to Tools,
Electronics or Sports, X-Cross demonstrates robust performance, while requiring
about 50%-75% less fine-tuning data than LoRA to make fine-tuning effective.
Furthermore, X-Cross achieves significant improvement in accuracy over
alternative cross-domain baselines. Overall, X-Cross enables scalable and
adaptive cross-domain recommendations, reducing computational overhead and
providing an efficient solution for data-constrained environments.
681713aec075e49c1b22500e
Guy Hadad
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Haggai Roitman
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Yotam Eshel
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yotam eshel
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Bracha Shapira
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Bracha Shapira
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Bshapira
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Lior Rokach
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Lior Rokach
user
liorrokach
X-Cross: Dynamic Integration of Language Models for Cross-Domain
  Sequential Recommendation
As new products are emerging daily, recommendation systems are required to
quickly adapt to possible new domains without needing extensive retraining.
This work presents ``X-Cross'' -- a novel cross-domain
sequential-recommendation model that recommends products in new domains by
integrating several domain-specific language models; each model is fine-tuned
with low-rank adapters (LoRA). Given a recommendation prompt, operating layer
by layer, X-Cross dynamically refines the representation of each source
language model by integrating knowledge from all other models. These refined
representations are propagated from one layer to the next, leveraging the
activations from each domain adapter to ensure domain-specific nuances are
preserved while enabling adaptability across domains. Using Amazon datasets for
sequential recommendation, X-Cross achieves performance comparable to a model
that is fine-tuned with LoRA, while using only 25% of the additional
parameters. In cross-domain tasks, such as adapting from Toys domain to Tools,
Electronics or Sports, X-Cross demonstrates robust performance, while requiring
about 50%-75% less fine-tuning data than LoRA to make fine-tuning effective.
Furthermore, X-Cross achieves significant improvement in accuracy over
alternative cross-domain baselines. Overall, X-Cross enables scalable and
adaptive cross-domain recommendations, reducing computational overhead and
providing an efficient solution for data-constrained environments.
X-Cross is a novel cross-domain sequential-recommendation model using low-rank adapters to dynamically refine representations and adapt to new domains efficiently with minimal fine-tuning data and parameters.
cross-domain sequential-recommendation model domain-specific language models low-rank adapters LoRA fine-tuning representation refinement domain-specific nuances Amazon datasets cross-domain tasks accuracy computational overhead data-constrained environments