Design and Implementation of a Word Distribution Platform for Personalized Recommendations

The article introduces the concept of "guide" (导购) in e‑commerce, which helps users discover hot items or promotions, and explains that word distribution is a core component of such guide systems.

It describes the platform’s main purpose: to provide a unified word‑recommendation service (including bottom‑words, hot‑words, drop‑down words, ranking lists, etc.) that reduces duplicated development, lowers maintenance cost, and improves operational efficiency across business domains.

Background : In the digital era, search engines are essential, and personalized keyword recommendation can significantly reduce user effort and improve conversion.

Core Functions :

Bottom‑words, hot‑words, and drop‑down words – each with distinct implementation logic (passive vs. active triggers).

Bottom‑words and hot‑words rely on user behavior analysis (i2q, q2q) without explicit user input.

Drop‑down words require real‑time user input and keyword matching.

Platform Architecture :

Pre‑processing – optional user‑profile calls, exposure filtering, tokenization, caching, condition handling, AB testing.

Recall – DSL construction supporting prefix, BM25, exact match, array queries, logical operators (AND/OR), and multi‑field composition; supports both C and Elasticsearch engines with parallel/serial/combined execution.

Recall Fusion – merges multi‑path results using configurable strategies, prioritizing non‑fallback routes.

Ranking – SDK, neuron, and RS models score results (CTR, CVR) and combine scores via weighted aggregation.

Re‑ranking – synonym scattering and price‑filtering to improve relevance.

Resource Placement – prioritizes resource‑word slots (pre‑, mid‑, post‑position) and avoids conflicts with mixed words.

Cluster Isolation : Two isolation schemes were evaluated (HTTP + Dubbo vs. separate application names). The second scheme (separate application names with gateway routing) was chosen to avoid additional gateway overhead.

C++ Engine Migration : To unify storage and improve performance, the platform migrates data to a C++ engine that offers efficient prefix‑tree structures, memory management, scheduling, snapshot recovery, and dump logging.

Script Hot‑Deployment : Abstracts business logic into a common interface, packages it as a script, and deploys it via backend configuration and AB testing, enabling rapid strategy updates without service restarts.

Visualization & Diff Tools : Provides a visual backend for multi‑path recall configuration (replacing manual JSON editing) and a diff tool to compare strategy or word‑library versions.

Badcase Intervention : Describes manual correction of problematic search results to improve overall quality.

Scenario Distribution : Lists various business scenarios (community search, transaction search, recommendation, content guide, etc.) where the platform is applied.

Conclusion : The platform’s architectural upgrades, monitoring, throttling, and circuit‑breaker mechanisms ensure stability and scalability, and future work will integrate with algorithm and control platforms for richer cross‑domain capabilities.