When generating rating predictions using collaborative filtering, what is the significance of the weighted average rating formula?It averages the ratings without considering similarity. It calculates the median rating.It ensures all ratings are treated equally.It gives higher weight to ratings from more similar users.

Does the following code snippet correctly generate the top-N recommendations for a user using collaborative filtering? def collab_generate_top_N_recommendations(user, N=10, k=20):    if user not in user_sim_df.columns:        return book_ratings.groupby('title').mean().sort_values(by='rating', ascending=False).index[:N].to_list()    sim_users = user_sim_df.sort_values(by=user, ascending=False).index[1:k+1]    favorite_user_items = []    most_common_favorites = {}    for i in sim_users:        max_score = util_matrix_norm.loc[:, i].max()        favorite_user_items.append(util_matrix_norm[util_matrix_norm.loc[:, i] == max_score].index.tolist())    for item_collection in range(len(favorite_user_items)):        for item in favorite_user_items[item_collection]:            if item in most_common_favorites:                most_common_favorites[item] += 1            else:                most_common_favorites[item] = 1    sorted_list = sorted(most_common_favorites.items(), key=operator.itemgetter(1), reverse=True)[:N]    top_N = [x[0] for x in sorted_list]    return top_NNo, it does not consider the rating values.Yes, it correctly generates top-N recommendations.No, it lacks the calculation of user similarity.No, it does not handle the 'Cold-start problem' properly.

Which type of collaborative filtering algorithm utilizes item-item similarities to generate recommendations?Question 23Answera. Item-based collaborative filteringb.Matrix factorizationc.Content-based filteringd.User-based collaborative filtering

When using the factor-rating method to assist in a decision-making process, it is important to assign weights to each criterion beforescoring the supplier options, thereby eliminating possible manipulation of the process.

Which of the following techniques is commonly used in Recommendation Systems to suggest items based on user similarities?Review LaterCollaborative FilteringMatrix FactorizationContent-Based FilteringHybrid Filtering

Briefly describe the difference between attitude rating scales and ranking scales andindicate when the two are used.