Propose new functionalities for Decidim software

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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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