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Propose new functionalities for Decidim software

#DecidimRoadmap Designing Decidim together

Phase 1 of 1
Open 2019-01-01 - 2030-12-31
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Intelligent recommendations

Avatar: DataForGoodBCN DataForGoodBCN verified-badge
30/06/2020 18:12  
Accepted / In progress

When someone publishes a new proposal, a list of similar entries is displayed to avoid duplicates. The current recommendation algorithm calculates the similarity of each pair of proposals based on trigram (sets of 3-characters) comparison. This method, however, does not take into account the semantic aspects of the text and can be easily improved using simple Machine Learning techniques.

We suggest using a technique called word embeddings which consists of assigning to each proposal a multi-dimensional vector, in such a way that similar proposals (in terms of semantics) end up having close vectors. Therefore, the recommendations for a given proposal would be the proposals with the smallest distances between the vectors.

To calculate the vectors associated with each proposal, we suggest using pre-calculated vector embeddings for each word (of those more frequent in the Decidim vocabulary) and then calculating the average of all words appearing in the proposal. The pre-calculation of word vectors could be done offline by any person with medium knowledge of NLP (DataForGoodBCN, the community that has created this proposal, could provide these calculations).



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This proposal has been accepted and is under development

List of Endorsements

Avatar: Platoniq Platoniq verified-badge
Avatar: Decidim Product Decidim Product verified-badge
Avatar: Laura Portell Laura Portell
Avatar: Didac Fortuny Didac Fortuny
Avatar: Pauline Bessoles Pauline Bessoles verified-badge
Avatar: Antoine Gaboriau Antoine Gaboriau
Avatar: Xavi Ros Roca Xavi Ros Roca
Avatar: Pierre Mesure Pierre Mesure verified-badge
Avatar: Felipe Álvarez Felipe Álvarez
Avatar: txema txema verified-badge
Avatar: Arnau Arnau
Avatar: Pau Parals Pau Parals verified-badge
Avatar: Quentin Lp Quentin Lp
Avatar: Pablo Aragón Pablo Aragón verified-badge
Avatar: Carol Romero Carol Romero verified-badge
Avatar: Oliver Azevedo Barnes Oliver Azevedo Barnes
Avatar: Ivan Vergés Ivan Vergés verified-badge
and 14 more people (see more) (see less)
Endorsements count17
Intelligent recommendations Comments 6

Reference: MDC-PROP-2020-06-15589
Version number 2 (of 2) see other versions
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Source: {"body":{"en":"<p>When someone publishes a new proposal, a list of similar entries is displayed to avoid duplicates. The current recommendation algorithm calculates the similarity of each pair of proposals based on trigram (sets of 3-characters) comparison. This method, however, does not take into account the semantic aspects of the text and can be easily improved using simple Machine Learning techniques.</p><p>We suggest using a technique called <strong>word embeddings</strong> which consists of assigning to each proposal a multi-dimensional vector, in such a way that similar proposals (in terms of semantics) end up having close vectors. Therefore, the recommendations for a given proposal would be the proposals with the&nbsp;smallest distances between the vectors.</p><p>To calculate the vectors associated with each proposal, we suggest using pre-calculated vector embeddings for each word (of those more frequent in the Decidim vocabulary) and then calculating the average of all words appearing in the proposal. The pre-calculation of word vectors could be done offline by any person with medium knowledge of NLP (DataForGoodBCN, the community that has created this proposal, could provide these calculations).</p><p><br></p><p><br></p>"},"title":{"en":"Intelligent recommendations"}}

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Avatar: Decidim Product Decidim Product verified-badge
18/01/2021 15:48
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Related to Improve automatic comparison algorithm when submiting a proposal

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