写一个联合推荐策略和缓存策略的算法，并用matlab实现

下面是一个简单的联合推荐策略和缓存策略的算法，使用Matlab实现：

1. 数据准备

我们将使用MovieLens数据集作为我们的样本数据。这个数据集包含了用户对电影的评分，我们将使用这些评分来进行推荐。

下载地址：https://grouplens.org/datasets/movielens/

2. 数据预处理

我们需要将数据集分成训练集和测试集。我们将选择80%的数据用于训练，剩下的20%用于测试。我们还需要将评分矩阵转换成二进制矩阵，其中1表示用户对该电影有评分，0表示没有评分。

以下是Matlab代码：

% Load data
data = load('ratings.csv');
data = data(:, 1:3);

% Split data into train and test sets (80/20 split)
rng(1); % For reproducibility
cv = cvpartition(size(data, 1), 'HoldOut', 0.2);
idx = cv.test;

trainData = data(~idx,:);
testData = data(idx,:);

% Create binary rating matrix
numUsers = max(data(:,1));
numMovies = max(data(:,2));

trainMatrix = zeros(numUsers, numMovies);
for i = 1:size(trainData, 1)
    trainMatrix(trainData(i,1), trainData(i,2)) = 1;
end

testMatrix = zeros(numUsers, numMovies);
for i = 1:size(testData, 1)
    testMatrix(testData(i,1), testData(i,2)) = 1;
end

3. 联合推荐策略

我们将使用基于用户和基于物品的推荐算法来生成推荐列表。然后，我们将联合这些列表来生成最终的推荐列表。

以下是Matlab代码：

% User-based recommendation
userSim = corr(trainMatrix');
userSim(isnan(userSim)) = 0;

userRecs = zeros(numUsers, numMovies);
for i = 1:numUsers
    [~, idx] = sort(userSim(i,:), 'descend');
    for j = 1:numMovies
        if trainMatrix(i,j) == 0
            userRecs(i,j) = sum(trainMatrix(idx(1:10),j)) / 10;
        end
    end
end

% Item-based recommendation
itemSim = corr(trainMatrix);

itemRecs = zeros(numUsers, numMovies);
for i = 1:numUsers
    for j = 1:numMovies
        if trainMatrix(i,j) == 0
            itemRecs(i,j) = sum(trainMatrix(i,:) .* itemSim(j,:)) / sum(itemSim(j,:));
        end
    end
end

% Joint recommendation
jointRecs = (userRecs + itemRecs) / 2;

4. 缓存策略

我们将使用一个简单的缓存策略来优化推荐性能。该策略将最近访问的电影添加到缓存中，并在下一次推荐时优先考虑缓存中的电影。

以下是Matlab代码：

% Cache policy
cacheSize = 10;
cache = zeros(cacheSize, 1);

for i = 1:numUsers
    % Get recommendation scores
    scores = jointRecs(i,:);
    
    % Check cache for top recommendations
    for j = 1:cacheSize
        [~, idx] = max(scores);
        if ismember(idx, cache)
            scores(idx) = -Inf;
        else
            cache(j) = idx;
            break;
        end
    end
    
    % Update jointRecs with cache
    for j = 1:cacheSize
        if cache(j) > 0
            scores(cache(j)) = Inf;
        end
    end
    
    [~, idx] = sort(scores, 'descend');
    jointRecs(i,:) = zeros(1, numMovies);
    jointRecs(i,idx(1:10)) = 1;
end

完整代码：

% Load data
data = load('ratings.csv');
data = data(:, 1:3);

% Split data into train and test sets (80/20 split)
rng(1); % For reproducibility
cv = cvpartition(size(data, 1), 'HoldOut', 0.2);
idx = cv.test;

trainData = data(~idx,:);
testData = data(idx,:);

% Create binary rating matrix
numUsers = max(data(:,1));
numMovies = max(data(:,2));

trainMatrix = zeros(numUsers, numMovies);
for i = 1:size(trainData, 1)
    trainMatrix(trainData(i,1), trainData(i,2)) = 1;
end

testMatrix = zeros(numUsers, numMovies);
for i = 1:size(testData, 1)
    testMatrix(testData(i,1), testData(i,2)) = 1;
end

% User-based recommendation
userSim = corr(trainMatrix');
userSim(isnan(userSim)) = 0;

userRecs = zeros(numUsers, numMovies);
for i = 1:numUsers
    [~, idx] = sort(userSim(i,:), 'descend');
    for j = 1:numMovies
        if trainMatrix(i,j) == 0
            userRecs(i,j) = sum(trainMatrix(idx(1:10),j)) / 10;
        end
    end
end

% Item-based recommendation
itemSim = corr(trainMatrix);

itemRecs = zeros(numUsers, numMovies);
for i = 1:numUsers
    for j = 1:numMovies
        if trainMatrix(i,j) == 0
            itemRecs(i,j) = sum(trainMatrix(i,:) .* itemSim(j,:)) / sum(itemSim(j,:));
        end
    end
end

% Joint recommendation
jointRecs = (userRecs + itemRecs) / 2;

% Cache policy
cacheSize = 10;
cache = zeros(cacheSize, 1);

for i = 1:numUsers
    % Get recommendation scores
    scores = jointRecs(i,:);
    
    % Check cache for top recommendations
    for j = 1:cacheSize
        [~, idx] = max(scores);
        if ismember(idx, cache)
            scores(idx) = -Inf;
        else
            cache(j) = idx;
            break;
        end
    end
    
    % Update jointRecs with cache
    for j = 1:cacheSize
        if cache(j) > 0
            scores(cache(j)) = Inf;
        end
    end
    
    [~, idx] = sort(scores, 'descend');
    jointRecs(i,:) = zeros(1, numMovies);
    jointRecs(i,idx(1:10)) = 1;
end

% Evaluate performance
precision = sum(sum(jointRecs .* testMatrix)) / sum(sum(jointRecs));
recall = sum(sum(jointRecs .* testMatrix)) / sum(sum(testMatrix));
f1 = 2 * precision * recall / (precision + recall);

fprintf('Precision: %.4f\n', precision);
fprintf('Recall: %.4f\n', recall);
fprintf('F1 Score: %.4f\n', f1);