How To Use Vector Search To Quickly Build A Content-Based Filtering Recommender System
Last Updated on July 17, 2023 by Editorial Team
Author(s): ___
Originally published on Towards AI.
Qualitative Evaluation
Visualizing some results (all movie posters are from imdb)
In this article, I will share how a vector search engine like weaviate can be used to quickly build a content-based filtering recommender system.
The code to reproduce the solution described in this article is in this repo.
We will demonstrate how the solution works using the MovieLens 1M Dataset.
A movie has the following properties:
TitlePlotSummaryGenres (list of strings)
The solution is really simple.
Given a movie and its properties, we simply turn it into a single vector using the sentence-transformers/msmarco-distilroberta-base-v2 model and store the results in the database.
To generate a recommendation, we take:
the last n movies… Read the full blog for free on Medium.
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