title: X-Cross: Dynamic Integration of Language Models for Cross-Domain
    Sequential Recommendation
summary: 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.
paper_authors._id: 681713aec075e49c1b22500e
paper_authors.name: Guy Hadad
paper_authors.status: extracted_confirmed
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paper_authors.fullname: guy hadad
paper_authors.type: user
paper_authors.user: guyhadad01
paper_authors._id: 681713aec075e49c1b22500f
paper_authors.name: Haggai Roitman
paper_authors._id: 681713aec075e49c1b225010
paper_authors.name: Yotam Eshel
paper_authors.status: extracted_pending
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paper_authors._id: 638f42e4c4444c6ca8715a06
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paper_authors.fullname: yotam eshel
paper_authors.type: user
paper_authors.user: yeshel
paper_authors._id: 681713aec075e49c1b225011
paper_authors.name: Bracha Shapira
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:35:41.141Z
paper_authors._id: 63aace84785b8279fe30b5f9
paper_authors.avatarUrl: /avatars/7b4793f6f0a0a8b608d0395c0e92a7eb.svg
paper_authors.fullname: Bracha Shapira
paper_authors.type: user
paper_authors.user: Bshapira
paper_authors._id: 681713aec075e49c1b225012
paper_authors.name: Lior Rokach
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:35:47.274Z
paper_authors._id: 64141f0365f4b23aa99507a4
paper_authors.avatarUrl: /avatars/46d599acaa0f492139949dba0f00e030.svg
paper_authors.fullname: Lior Rokach
paper_authors.type: user
paper_authors.user: liorrokach
paper_title: X-Cross: Dynamic Integration of Language Models for Cross-Domain
    Sequential Recommendation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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