Vol. 6 No. 2 (2026): July−December 2026
Articles

Profile Verbalization for Interaction-Free Cold-Start Recommendation: A Cross-Domain Empirical Evaluation

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  • Yunqiang Sun,
  • Zhouting Mo,
  • Yinchu Wu
Yunqiang Sun
Computer Science and Technology, University of Jinan, Shandong, China
Zhouting Mo
Computer Technology, Fudan University, Shanghai, China
Yinchu Wu
Computer Technology, Shanghai Jiao Tong University, Shanghai, China

Published 2026-09-29

Keywords

  • cold-start recommendation,
  • profile verbalization,
  • zero-interaction evaluation,
  • semantic matching

How to Cite

Sun, Y., Mo, Z., & Wu, Y. (2026). Profile Verbalization for Interaction-Free Cold-Start Recommendation: A Cross-Domain Empirical Evaluation. Spectrum of Research, 6(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/65

Copyright (c) 2026 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

When neither user nor item interactions are available, profile text becomes the only observable bridge between the two sides of a recommendation decision. This study examines whether the form of that text changes zero-interaction ranking quality. Four deterministic verbalization strategies—values only, key-value serialization, full natural-language sentences, and preference-focused compression—are evaluated on MovieLens 100K, In-Vehicle Coupon, and Restaurant & Consumer Data. A frozen 384-dimensional sentence encoder maps user and item profiles independently, and cosine similarity ranks 112,945 held-out pairs without target-domain fitting, demonstration selection, or threshold tuning. The results reveal no domain-general winner. Values-only serialization is effectively random on MovieLens (ROC-AUC 0.498); full sentences perform best for coupons (0.556); and key-value text performs best for restaurants (0.533). Preference-focused compression reduces the mean pair length by 39.7% to 84.7% relative to key-value text, yet its ROC-AUC falls below 0.5 in every domain. PR-AUC exhibits the same domain dependence, with maxima of 0.554, 0.621, and 0.442. These findings indicate that fluent or shorter profiles are not intrinsically better matching interfaces. Explicit field identity helps when user and item attributes share semantics, while contextual propositions help when acceptance depends on a situation. Profile verbalization should be treated as an experimentally controlled representation choice rather than neutral preprocessing.

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