Software Alternatives & Reviews

Cloud Job Discovery VS Annoy

Compare Cloud Job Discovery VS Annoy and see what are their differences

Cloud Job Discovery logo Cloud Job Discovery

We're broadening the reach of Cloud Job Discovery beyond job boards and career site providers to staffing agencies and applicant tracking systems. We’re also introducing support for job search in 100+ language varieties.

Annoy logo Annoy

Annoy is a C++ library with Python bindings to search for points in space that are close to a given query point.
  • Cloud Job Discovery Landing page
    Landing page //
    2023-01-30
  • Annoy Landing page
    Landing page //
    2023-10-10

Cloud Job Discovery

Categories
  • Data Science And Machine Learning
  • Data Dashboard
  • Javascript UI Libraries
Website blog.google

Annoy

Categories
  • Utilities
  • Twitter Tools
  • Twitter Automation
  • Social Media Marketing
Website github.com

Cloud Job Discovery videos

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Annoy videos

Does Asking for Reviews Annoy My Customers?

More videos:

  • Review - Why Timex Watches Annoy Me | Timex Would Dominate the Market If They Just...
  • Demo - Annoy-a-tron Demonstration

Category Popularity

0-100% (relative to Cloud Job Discovery and Annoy)
Data Dashboard
100 100%
0% 0
Utilities
0 0%
100% 100
Data Science And Machine Learning
Twitter Tools
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, Annoy seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Cloud Job Discovery mentions (0)

We have not tracked any mentions of Cloud Job Discovery yet. Tracking of Cloud Job Discovery recommendations started around Mar 2021.

Annoy mentions (35)

  • Do we think about vector dbs wrong?
    The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy. Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy), an open source embeddings database. - Source: Hacker News / 8 months ago
  • Vector Databases 101
    If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw. Source: 10 months ago
  • Calculating document similarity in a special domain
    I then use annoy to compare them. Annoy can use different measures for distance, like cosine, euclidean and more. Source: 11 months ago
  • Can Parquet file format index string columns?
    Yes you can do this for equality predicates if your row groups are sorted . This blog post (that I didn't write) might add more color. You can't do this for any kind of text searching. If you need to do this with file based storage I'd recommend using a vector based text search and utilize a ANN index library like Annoy. Source: 11 months ago
  • [D]: Best nearest neighbour search for high dimensions
    If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy). Source: 11 months ago
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When comparing Cloud Job Discovery and Annoy, you can also consider the following products

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Vectara Neural Search - Neural search as a service API with breakthrough relevance

AWS Personalize - Real-time personalization and recommendation engine in AWS