Simulation of Recommendation Systems Gaming Project in C++

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#include <iostream>
#include <vector>
#include <cmath>
#include <iomanip>

const int NUM_USERS = 5;
const int NUM_ITEMS = 4;

// Function to compute the similarity between two users using cosine similarity
double computeSimilarity(const std::vector<double>& user1, const std::vector<double>& user2) {
    double dotProduct = 0.0;
    double norm1 = 0.0;
    double norm2 = 0.0;

    for (int i = 0; i < NUM_ITEMS; ++i) {
        dotProduct += user1[i] * user2[i];
        norm1 += user1[i] * user1[i];
        norm2 += user2[i] * user2[i];
    }

    norm1 = std::sqrt(norm1);
    norm2 = std::sqrt(norm2);

    if (norm1 == 0.0 || norm2 == 0.0) {
        return 0.0;
    }

    return dotProduct / (norm1 * norm2);
}

// Function to recommend items for a user based on similarities with other users
void recommendItems(const std::vector<std::vector<double>>& ratings, int targetUser) {
    std::vector<double> userRatings = ratings[targetUser];
    std::vector<double> itemScores(NUM_ITEMS, 0.0);
    std::vector<double> totalSimilarities(NUM_ITEMS, 0.0);

    for (int i = 0; i < NUM_USERS; ++i) {
        if (i == targetUser) continue;

        double similarity = computeSimilarity(ratings[targetUser], ratings[i]);

        for (int j = 0; j < NUM_ITEMS; ++j) {
            if (ratings[i][j] > 0) {
                itemScores[j] += similarity * ratings[i][j];
                totalSimilarities[j] += similarity;
            }
        }
    }

    std::cout << "Recommendations for User " << targetUser + 1 << ":\n";
    for (int i = 0; i < NUM_ITEMS; ++i) {
        if (userRatings[i] == 0) { // Only recommend items not yet rated by the user
            double predictedRating = (totalSimilarities[i] > 0) ? itemScores[i] / totalSimilarities[i] : 0.0;
            std::cout << "Item " << i + 1 << ": " << std::fixed << std::setprecision(2) << predictedRating << "\n";
        }
    }
}

int main() {
    // Example ratings matrix (users x items)
    std::vector<std::vector<double>> ratings = {
        {4, 0, 0, 2},
        {0, 5, 0, 0},
        {3, 0, 0, 0},
        {0, 0, 4, 0},
        {5, 0, 0, 0}
    };

    int targetUser;
    std::cout << "Enter the user number (1-" << NUM_USERS << ") for recommendations: ";
    std::cin >> targetUser;

    if (targetUser < 1 || targetUser > NUM_USERS) {
        std::cerr << "Invalid user number.\n";
        return 1;
    }

    recommendItems(ratings, targetUser - 1);

    return 0;
}

#include <iostream>

#include <vector>

#include <cmath>

#include <iomanip>

const int NUM_USERS = 5;

const int NUM_ITEMS = 4;

// Function to compute the similarity between two users using cosine similarity

double computeSimilarity(const std::vector<double>& user1, const std::vector<double>& user2) {

double dotProduct = 0.0;

double norm1 = 0.0;

double norm2 = 0.0;

for (int i = 0; i < NUM_ITEMS; ++i) {

dotProduct += user1[i] * user2[i];

norm1 += user1[i] * user1[i];

norm2 += user2[i] * user2[i];

}

norm1 = std::sqrt(norm1);

norm2 = std::sqrt(norm2);

if (norm1 == 0.0 || norm2 == 0.0) {

return 0.0;

}

return dotProduct / (norm1 * norm2);

}

// Function to recommend items for a user based on similarities with other users

void recommendItems(const std::vector<std::vector<double>>& ratings, int targetUser) {

std::vector<double> userRatings = ratings[targetUser];

std::vector<double> itemScores(NUM_ITEMS, 0.0);

std::vector<double> totalSimilarities(NUM_ITEMS, 0.0);

for (int i = 0; i < NUM_USERS; ++i) {

if (i == targetUser) continue;

double similarity = computeSimilarity(ratings[targetUser], ratings[i]);

for (int j = 0; j < NUM_ITEMS; ++j) {

if (ratings[i][j] > 0) {

itemScores[j] += similarity * ratings[i][j];

totalSimilarities[j] += similarity;

}

std::cout << "Recommendations for User " << targetUser + 1 << ":\n";

for (int i = 0; i < NUM_ITEMS; ++i) {

if (userRatings[i] == 0) { // Only recommend items not yet rated by the user

double predictedRating = (totalSimilarities[i] > 0) ? itemScores[i] / totalSimilarities[i] : 0.0;

std::cout << "Item " << i + 1 << ": " << std::fixed << std::setprecision(2) << predictedRating << "\n";

}

int main() {

// Example ratings matrix (users x items)

std::vector<std::vector<double>> ratings = {

{4, 0, 0, 2},

{0, 5, 0, 0},

{3, 0, 0, 0},

{0, 0, 4, 0},

{5, 0, 0, 0}

};

int targetUser;

std::cout << "Enter the user number (1-" << NUM_USERS << ") for recommendations: ";

std::cin >> targetUser;

if (targetUser < 1 || targetUser > NUM_USERS) {

std::cerr << "Invalid user number.\n";

return 1;

}

recommendItems(ratings, targetUser - 1);

return 0;

}

Explanation

Constants:
- NUM_USERS: Number of users in the system.
- NUM_ITEMS: Number of items available for rating.
Function computeSimilarity(const std::vector<double>& user1, const std::vector<double>& user2):
- Purpose: Computes the cosine similarity between two users based on their item ratings.
- Parameters:
  - user1, user2: Vectors of ratings for two different users.
- Implementation:
  - Computes the dot product and norms of the vectors.
  - Calculates similarity using the formula dotProduct / (norm1 * norm2).
Function recommendItems(const std::vector<std::vector<double>>& ratings, int targetUser):
- Purpose: Recommends items to a specific user based on the ratings of other users.
- Parameters:
  - ratings: 2D vector representing the ratings matrix.
  - targetUser: Index of the user for whom recommendations are to be generated.
- Implementation:
  - Uses similarity scores to compute weighted item scores.
  - Predicts ratings for items not yet rated by the target user.
  - Outputs the recommended items with their predicted ratings.
Main Function:
- Setup: Defines an example ratings matrix.
- User Input: Prompts the user to enter the target user number.
- Recommendation: Calls recommendItems
  
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  to provide recommendations based on the target user’s preferences.

Usage

Recommendation System Simulation: Demonstrates a basic collaborative filtering approach to recommend items based on user similarities.
User Preferences: Shows how ratings from similar users can influence recommendations for an individual user.

Explanation

Usage

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