Method and system for classifying content of web pages using machine learning techniques

A non-transitory computer-readable medium storing a set of instructions for optimizing web content, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive contents of a web page, wherein the contents include at least a plurality of elements, each element including a markup language code;
• detect, using a semantic similarity technique, at least one relevant element from the contents based on a semantic similarity to an element in a predefined list of element types;
• generate at least one element feature vector in a vector database, wherein each feature of the at least one element feature vector represents an attribute of the at least one relevant element;
• classify, using a machine learning model trained on labeled examples of element feature vectors, the at least one element feature vector;
• identify at least one candidate element of the at least one relevant element for web content optimization based on the classification for the at least one candidate element and the element feature vector representing the at least one candidate element;
• generate markup language code for at least one web-optimized element, wherein generating the markup language code for the at least one web-optimized element replaces the at least one candidate element to change the contents of the web page;
• the device further to:
• detecting, using a semantic similarity technique, at least one collection of elements of the plurality of elements from the contents;
• generating at least one collection feature vector representing the at least one relevant collection in a vector database, wherein each feature of the least one collection feature vector represents an attribute of the at least one collection:
• classifying, using a machine learning model trained on labeled examples of collection feature vectors, the at least one collection feature vector;
• identifying at least one candidate collection of the at least one collection for web content optimization based on the classification for the at least one candidate collection and the collection feature vector representing the at least one candidate collection;
• and
• generating markup language code for at least one web-optimized collection, wherein generating the markup language code for the at least one web-optimized collection replaces the at least one candidate collection to change the contents of the web page.

A system for optimizing web content comprising:

• a processing circuitry;
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• receive contents of a web page, wherein the contents include at least a plurality of elements, each element including a markup language code;
• detect, using a semantic similarity technique, at least one relevant element from the contents based on a semantic similarity to an element in a predefined list of element types;
• generate at least one element feature vector in a vector database, wherein each feature of the at least one element feature vector represents an attribute of the at least one relevant element;
• classify, using a machine learning model trained on labeled examples of element feature vectors, the at least one element feature vector;
• identify at least one candidate element of the at least one relevant element for web content optimization based on the classification for the at least one candidate element and the element feature vector representing the at least one candidate element;
• and
• generate markup language code for at least one web-optimized element, wherein generating the markup language code for the at least one web-optimized element replaces the at least one candidate element to change the contents of the web page;
• the system further to:
• detect, using a semantic similarity technique, at least one collection of elements of the plurality of elements from the contents;
• generate at least one collection feature vector in a vector database, wherein each feature of the least one collection feature vector represents an attribute of the at least one collection;
• classify, using a machine learning model trained on labeled examples of collection feature vectors, the at least one collection feature vector;
• identify at least one candidate collection of the at least one collection for web content optimization based on the classification for the at least one candidate collection and the collection feature vector;
• and
• generate markup language code for at least one web-optimized collection, wherein generating the markup language code for the at least one web-optimized collection replaces the at least one candidate collection to change the contents of the web page.