{"uid":"cap_Cc3mDqblGPAxzXxROAIWT","slug":"vektorwerk-co-occurrence-recommender-e0d55397","name":"Vektorwerk Co-occurrence Recommender","description":"Calculates item co-occurrence and item similarity from the submitted events only. No user profile is stored.","url":"https://vektor.halowerk.com/recommend","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"limit":{"type":"integer","default":5,"maximum":50,"minimum":1},"item_id":{"type":"string","example":"item-a","maxLength":120,"minLength":1},"interactions":{"type":"array","items":{"type":"object","required":["user_id","item_id"],"properties":{"weight":{"type":"number","default":1,"maximum":100,"minimum":0},"item_id":{"type":"string","example":"item-a","maxLength":120,"minLength":1},"user_id":{"type":"string","example":"user-1","maxLength":120,"minLength":1}},"additionalProperties":false},"maxItems":2000,"minItems":1},"user_history":{"type":"array","items":{"type":"string","maxLength":120,"minLength":1},"example":["item-a"],"maxItems":100}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_qkoz5OE_ByFaKH1PC4XkW","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Calculates item co-occurrence and similarity from submitted interaction events to return recommendations, without storing any user profiles.","exampleAgentPrompt":"Using the interaction events I'm sending you — where user-1 viewed item-a, item-b, and item-c, and user-2 viewed item-a and item-d — give me the top 5 items most similar to item-a based on co-occurrence, without storing any user profiles.","exampleUseCases":[{"title":"E-commerce related products widget","prompt":"I have a list of 500 recent purchase events across my users and I want to show the top 3 items most commonly bought alongside product SKU-789 — can you compute that co-occurrence and give me recommendations without keeping any user data?"},{"title":"Content platform 'watch next' suggestions","prompt":"Based on these 200 viewing interactions I'm passing in, what are the top 5 videos most similar to video-42 that I should suggest as 'watch next' to someone who already watched video-10 and video-15?"},{"title":"Privacy-safe playlist recommendation","prompt":"I want to recommend 8 songs similar to track-moon-river using only the listening events I'm about to send — no persistent profiles allowed — can you rank them by co-occurrence similarity?"}],"resultDescription":"Returns a ranked list of recommended item IDs (up to the requested limit, max 50) scored by co-occurrence and similarity computed purely from the submitted interaction events. No data is persisted between calls.","failureModes":["interactions array is empty or missing — minItems:1 required","item_id not found in the submitted interactions — returns empty or low-confidence recommendations","interactions array exceeds 2000 events — request rejected","limit out of range (must be 1–50) — validation error","item_id or user_id strings exceed 120 characters — schema validation failure","weight values outside 0–100 range — rejected","malformed JSON body — 400 error"],"whenToPreferThis":"Choose this endpoint when you need stateless, privacy-preserving item recommendations computed entirely from a single request's interaction events — ideal when you cannot or do not want to store user profiles server-side. Prefer it for collaborative-filtering-style co-occurrence recommendations (not content-based or embedding-based similarity). It is well-suited for e-commerce, content platforms, or any domain where GDPR/privacy constraints prohibit persistent user data. If you need embedding-based semantic similarity rather than behavioral co-occurrence, consider the sibling embedding endpoints on the same host.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:34:16.234Z","isFirstParty":false}