Untitled

def get_recommended_jobs(user,heta=0.001,K=20, precision=0.00001, per_view_score = 5., number_of_recommendations = 20):
    users = User.query.all()
    user_count = User.query.count() #its faster to reconstruct the query
    
    
    #map id to matrix index
    user_id_to_index_map = {}
    index_to_user_id_map = {}
    for i in range(user_count):
        user_id_to_index_map.update({users[i].id:i})
        index_to_user_id_map.update({i:users[i].id})

    job_postings = JobPostings.query.all()
    job_postings_count = JobPostings.query.count()

    #the recommender system is useful only if it can pick out a small portion of the total number of job postings
    if number_of_recommendations > job_postings_count/3 + 1:
        number_of_recommendations = job_postings_count/3 + 1
    
    #map job_id to matrix index
    job_id_to_index_map = {}
    index_to_job_id_map = {}
    for i in range(job_postings_count):
        job_id_to_index_map.update({job_postings[i].id:i})
        index_to_job_id_map.update({i:job_postings[i].id})


    print(user_count,job_postings_count)


    
    #build the matrix for known job posting views and the list of known indexes
    X = np.zeros( (user_count,job_postings_count) )
    
    visits = db.session.query(user_visited_job_posting).all()
    known_indexes = []
    for (uid,jid) in visits:
        X[ user_id_to_index_map[uid], job_id_to_index_map[jid] ] = per_view_score
        known_indexes.append( (user_id_to_index_map[uid], job_id_to_index_map[jid]) )

    
    #generate ranom V,F matrices
    V = per_view_score * np.random.rand(user_count,K)
    F = per_view_score * np.random.rand(K,job_postings_count)

    (V,F) = MF_RS(X,V,F,known_indexes,heta,K,precision)

    X_ = V @ F

    #get recommendations for the user
    recomendations = []
    user_index = user_id_to_index_map[user.id]

    #recommend the first number_of_recommendations jobs that have a positive rating
    for i in range(number_of_recommendations):
        job_index = np.argmax(X_[user_index,:])
        if X_[user_index,job_index] > 0 :
            if job_postings[job_index].active is True and job_postings[job_index].creator_id != user.id:
                recomendations.append( job_postings[ job_index ] )
            #don't pick that job posting again
            X_[user_index,job_index] = -1.
        else:
            break
    
    return recomendations