# Data Science at FMP: Dataset Recommendation

DevFeed: [Data Science at FMP: Dataset Recommendation](<https://devfeed.tech/articles/data-science-at-fmp-dataset-recommendation-19741.md>)

Original publisher: [Read original article](<https://tech.findmypast.com/datsci-dataset-rec0/>)

Author: Tom Thurstan

Published: 2021-12-23T00:00:00Z

Content type: article

Language: en

Sources: [Findmypast](<https://devfeed.tech/sources/findmypast.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [product](<https://devfeed.tech/tags/product.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [research](<https://devfeed.tech/tags/research.md>)

## AI overview

Findmypast's data science team explores recommendation systems for helping users discover datasets relevant to their research. The article compares collaborative filtering and content-based filtering, explains why content-based filtering is unsuitable for this domain, and describes limitations caused by the lack of user ratings before introducing another approach.

## Source excerpt

Problem Outline One of the problems we recently tackled in the data science team was how we could introduce our users to new datasets they may not be familiar with. We wanted these datasets to be personal to each user and relevant to their research areas. A recommendation system of some sort was the obvious way to solve this problem. Recommendation Systems Recommendation systems come with several possible methodologies. Two of the most widely known methodologies are Collaborative Filtering (which can be split into two branches, user-based or item-based) and Content-Based Filtering. User-based collaborative filtering recommendations are based on finding users with similar preferences and recommending items that similar users rated highly but have not been viewed by the target user. E.g., "Users who liked similar items to you also liked..." For item-based collaborative filtering, instead of finding similar users we find items that have been rated similarly by different users and recommended based on a user's previous preferences. E.g., "Users that liked this item also liked..." An important part of the data required for these techniques is ratings, so you can tell which items users liked. Another popular technique is Content-Based Filtering. This technique requires less information about each individual user and more about the items themselves. The recommendations are created by finding items that have similar characteristics, e.g., movies of the same genre, with the same actors, or directors. Then given a user's previous preferences you can recommend items similar to items they previously enjoyed. A broad and consistent range of metadata is needed for each of the items and again ratings are useful for this methodology. Within our domain at Findmypast a Content-Based Filtering system would not be appropriate. It is easy to see that some datasets which would share a lot of metadata would not necessarily make good recommendations. E.g., Kentucky Birth Records would most lik