TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings

• URL: http://arxiv.org/abs/2607.28940v1
• Date: Fri, 31 Jul 2026 01:42:57 GMT
• Title: TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings
• Abstract summary: We propose TransX, a production-oriented encoder-decoder architecture that reformulates recommendation as a sequence-to-sequence action problem.<n>To enable low-latency, high-QPS deployment, TransX is co-designed with an amortized serving strategy that combines incremental behavior encoding with per-request key-value caching.
• Score: 4.750812376477589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern industrial recommender systems (RecSys) increasingly adopt Transformer-based sequence models, with an emerging paradigm that frames recommendation as next-token prediction over a unified monolithic user sequence. However, collapsing heterogeneous data sources -- such as long-term user behaviors and real-time serving events -- into a single monolithic token stream that obscures their distinct causal roles and temporal characteristics, leading to inefficient modeling and elevated training and serving costs. We propose TransX, a production-oriented encoder-decoder architecture that reformulates recommendation as a sequence-to-sequence action transduction problem. TransX explicitly decouples behavior-stream modeling from serving-event modeling and conditions next-action decoding on scalable cross-attention between nearline behavior encodings and real-time serving representations. To enable low-latency, high-QPS deployment, TransX is co-designed with an amortized serving strategy that combines incremental behavior encoding with per-request key-value caching, rendering serving latency insensitive to behavior sequence length. Extensive offline experiments and large-scale online A/B tests on LinkedIn's recommender systems show that TransX consistently outperforms state-of-the-art DLRMs and sequential baselines, and delivers substantial CTR lift (+6.0%) and conversion gain (+4.4%) while maintaining serving costs comparable to existing production models where our co-designed serving strategy reduces online computation by approximately 80%.

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