How Viking AI Search Uses Personalization to Boost Conversion

For many developers and small teams, a basic search function only returns items that match the query, which can lead to results that are relevant but not aligned with individual user preferences—for example, a southern user searching for "down jacket" may prefer a lightweight style, while a northern user may want a heavy, warm version.

Viking AI Search addresses this gap with a simple two‑step approach: first guarantee textual relevance, then incorporate "item hotness" and "user interest" signals to adjust recall and ranking, producing results that stay on topic while reflecting current user tastes.

The search experience is divided into four stages: recall, ranking, re‑ranking, and operational intervention. The basic personalization features affect the first two stages by enabling personalized recall and allowing item hotness to influence ranking.

Item hotness reflects collective preference: the system calculates a hotness score from user actions such as clicks, adds to cart, purchases, comments, favorites, and shares. Popular items gain higher ranking opportunities on top of relevance. For instance, when users search for "camping lamp," both an old, rarely viewed product and a newly popular lamp appear; hotness ensures the latter, validated by many users, surfaces earlier.

Personalized recall builds on hotness by adding a user‑interest channel. Viking AI generates interest tags from recent behavior, covering categories, brands, and styles. If a user frequently interacts with "sports shoes," "Adidas," and "commute style," the system tags these preferences and, when the user later searches for "shoes" or "coat," it boosts items matching those tags.

The key principle is that personalization does not replace relevance; it adds a "user‑interest recall" path while still respecting the original query.

From a technical standpoint, developers upload a behavior‑data set, configure item fields (category, brand, style, etc.) as filterable attributes, then enable personalized recall and choose an intervention level in the console.

Two intervention modes are available: weak (conservative, keeping keyword and semantic matching dominant while lightly boosting interest‑matched items) and strong (giving higher weight to interest tags). New adopters are advised to start with weak intervention, evaluate click‑through, conversion, and dwell time, and then adjust.

The solution is described with three attributes:

Simple : No need to train complex CTR models; developers only upload data, configure fields, and toggle switches.

Effective : By continuously feeding user actions into the ranking, the system surfaces items users truly want, improving click‑through and conversion rates.

Controllable : Hotness and personalization settings are adjustable in the console, allowing teams to test strategies before full deployment.

For e‑commerce mini‑programs, vertical content communities, asset libraries, knowledge bases, or product recommendation apps, search is often the primary entry point. Viking AI Search’s personalization turns a generic "search‑to‑find" experience into a tailored, conversion‑driving layer.