photovoltaic systems book

descriptionAdvantages:(1) There is no need for large-scale users (like collaborative filtering) to get the relationship between items(2) Once the property of the item is obtained, the item can be recommended to the user immediately.3. Knowledge-based recommendationsIn some areas, such as the consumer electronics sector (e.g), the vast majority of data is a single purchase record. If you apply both of these methods, the data is too sparse to even get the recommended results. What if we have to r

When I first saw the first version of this book, it seemed like it was in, and I was shocked when I remembered it. I was able to reach this level as a teaching material for my undergraduate sophomore year, and I also got in touch with a lot of content for the first time, I was very ashamed. Later, I spent some time studying it carefully. Unfortunately, there were no specific experiments on many important points. At that time, I felt that this

understand, seemingly and the above--when a feature OK, use this feature; , the weaker features are used2. Parallel hybrid design Multiple recommendation engines, how to fuse together? 2.1 Cross-mixing multiple results of multiple recommendation engines, cross-merge into one result: first engine first result ranked first, second engine first result ranked second ... 2.2 Weighted mixed linear weighted combination, one weight per engine, weight normalization 2.3 switching mix when in some cases w

Basic idea: The data is divided into training set and test set, training model with training set data, test model with test set data. The Division of Training set and test set can be by the dimension of time, or by the dimension of the crowd. Risk: There may be biases for some methods.Using historical data to evaluate the data into training set test set and N-fold cross-validation according to Time dimension.There is also the direct use of human evaluation. However, the cost is larger, not on th