Explorentory is a full-stack, AI-powered NYC rental discovery platform. Rather than mimicking the filter-first paradigm of traditional real estate search — where users must commit to a price cap and bedroom count before seeing anything — Explorentory inverts the process. Users express natural-language preferences, explore a neighborhood on a live map, rate a curated sample of real properties, and receive personalized recommendations ranked by an OLS regression model trained on their own feedback. Three LLM-powered endpoints provide plain-English context at every stage. The platform operates on a PostGIS database of approximately 3 million synthetic NYC units derived from PLUTO, with MapLibre GL rendering up to 5,000 results as an interactive choropleth map across 9 data dimensions.

Traditional real estate platforms — Zillow, StreetEasy, Redfin — are built around a filter-first paradigm: users must specify price, bedrooms, and neighborhood before seeing any results. This forces premature decisions and obscures trade-offs. If a user's exact criteria return zero results, they must manually adjust filters and re-search. Explorentory replaces filter walls with ranked recommendations. The system learns what users actually value from their rating behavior and surfaces properties that balance competing preferences, including ones the user never explicitly stated.

The experience is structured as a four-step guided flow. Each step collects a different layer of preference signal, from explicit numerical constraints, to geographic intent, to implicit behavioral feedback, progressively building the data needed to produce a personalized recommendation.

Explorentory is a three-tier system. A PostGIS spatial database holds approximately 3 million property records and 260 neighborhood polygons. A FastAPI backend exposes six REST endpoints — three for data retrieval and ML recommendation, three for OpenAI-powered natural language features. A Vanilla JavaScript frontend renders results with MapLibre GL and two custom Canvas-based charts, with no front-end framework dependency.

Explorentory includes two custom Canvas-based charts, built without a charting library. This allows fine-grained pixel control, seamless dark/bright mode switching, and eliminates a dependency on external rendering engines.

The recommendation engine combines a deterministic rule-based scorer with an OLS regression model trained in real time on the user's survey ratings. With only 10 training samples and 11 features, careful feature engineering and global scaling are essential to prevent overfitting.

Raw property attributes are converted to user-relative differences before training. For example, bedroomnum becomes |bedroomnum − target_bedrooms|, aligning the feature with what the user actually cares about — deviation from their preference — rather than the raw count. borocode_match is a binary flag indicating whether the property is in the user's chosen borough. All 11 engineered features are scaled with a StandardScaler fit on the entire 3M-record dataset, providing stable mean and variance that do not shift with 10-sample noise.

The hybrid approach serves two purposes. The rule-based component ensures predictable, interpretable behavior that respects user-stated priorities even when the ML signal is noisy — which is inevitable with only 10 training samples. The ML component captures implicit preferences that the user never explicitly stated: a consistent preference for newer buildings, quieter streets, or larger units that emerged from rating behavior. Averaging the two scores prevents either component from dominating and produces a more robust, balanced final ranking. OLS coefficients are passed to /explain_result and translated into natural language, giving users a legible account of what the model learned from their feedback.