Memorization model for context violations

A non-transitory computer-readable medium comprising computer program instructions that when executed by a computer processor of an online system causes the processor to perform steps comprising:

• generating a memorization model for predicting likelihood scores of context violations for content campaigns, the generating comprising:
• identifying a plurality of item feature vectors for a plurality of content campaigns with known context violations, each item feature vector determined from a respective category of content campaign characteristics from a plurality of categories;
• training an item weight for each of the plurality of item feature vectors of each category, the item weight indicating a relevance of each item feature vector when it matches a feature vector of a content campaign;
• and
• training a category weight for each category, the category weight indicating a relevance of each category when an item feature vector determined from the category matches a feature vector of a content campaign;
• and
• receiving a content campaign for content to be presented to users of the online system;
• identifying a plurality of feature vectors for the received content campaign, each feature vector of the plurality of feature vectors determined from a respective category from the plurality of categories;
• predicting a likelihood score of context violations of the content campaign using the memorization model based on the plurality of feature vectors, the predicting comprising:
• for each feature vector of the plurality of feature vectors:
• comparing the feature vector to the plurality of item feature vectors identified by the memorization model;
• identifying a matching item feature of the respective category of the feature vector based on the comparison;
• and
• generating a combination of a trained category weight of the respective category of the feature vector and a trained item weight of the matching item feature vector;
• determining the predicted likelihood score based on the combinations determined for the plurality of feature vectors;
• determining the received content campaign violates a context policy of the online system based on the predicted likelihood score being included in a likelihood score range;
• and
• performing an action corresponding to the likelihood score range, the action addressing the violation of the context policy by the content campaign.