Category : recommender-systems

I have a movie recommender system I have been working on and currently it is printing two different sets of output because I have two different types of recommendation engines. Code is like this: while True: user_input3 = input(‘Please enter movie title: ‘) if user_input3 == ‘done’: break try: print(‘ ‘) print(get_input_movie(user_input3)) print(get_input_movie(user_input3, cosine_sim1)) # ..

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I don’t know how to write a code to load a CSV file or .inter file instead of the built in dataset in this example of evaluating a dataset as a recommender system: from surprise import SVD from surprise import KNNBasic from surprise import Dataset from surprise.model_selection import cross_validate # Load the movielens-100k dataset (download ..

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for research purposes I am looking for a Python library that would allow me to implement matrix factorisation, and allow playing with the definition of optimisation functions, e.g. the RMSE. Unfortunately, after almost a day of googling I was not able to find a flexible enough library. The Python language constraint is not vital, but ..

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I’m new to recommender models and I’m using LightFM for a project. I’m creating model for customer like/dislike recommendations (no ratings involved). Are there any options for model interpretability in such cases? I understand LightFM is a hybrid approach (content based & collaborative), but is there a way I can rank user/item features on the ..

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I am working on a process for Clothing Recommendation System. Firstly, I got a problem with creating User Profiles. Does any body know about it def get_item_profile(item_id): idx = item_ids.index(item_id) item_profile = tfidf_matrix[idx:idx+1] return item_profile def get_item_profiles(ids): item_profiles_list = [get_item_profile(x) for x in ids] item_profiles = scipy.sparse.vstack(item_profiles_list) return item_profiles def build_users_profile(person_id, interactions_indexed_df): interactions_person_df = interactions_indexed_df.loc[person_id] ..

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I am currently building a recommender system and I am trying to evaluate it. However, many sources have differing methods of computing [email protected] and [email protected] Let me give an example for easier illustration. Suppose we only have 1 user with total of 8 items split into train and test for training and testing respectively. The ..

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