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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