Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Systems, methods, and non-transitory computer readable media configured to acquire data associated with a content item, the data associated with the content item including contextual information. The data associated with the content item can be provided to a model trained by machine learning. A set of hashtags associated with the content item can be determined based on the model.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Systems, methods, and non-transitory computer-readable media according to certain aspects can obtain a comment submitted by a user on a page associated with an entity. A training data set, including a plurality of comments, that indicates whether each of the plurality of comments is actionable can be determined. A machine learning model can be trained based on the training data set. Whether the comment is actionable can be determined based at least in part on the machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media according to certain aspects can receive at least one message sent by a user of a social networking system to a page provided by the social networking system, where the page is associated with an entity. A training data set including a plurality of messages can be determined, and the training data set can indicate an intent classification for each of the plurality of messages. The intent classification can be indicative of an intent associated with a particular message. A machine learning model may be trained based at least in part on the training data set. A first intent classification for the at least one message can be determined, based at least in part on the machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Systems, methods, and non-transitory computer readable media configured to acquire data associated with a content item, the data associated with the content item including contextual information. The data associated with the content item can be provided to a model trained by machine learning. A set of hashtags associated with the content item can be determined based on the model.

Abstract: A method includes: receiving a first set of records, each record received from a respective user in a first set of users, and including a set of tokens and a set of items, and kept secret from parties other than the respective user, evaluating the first set of records by a recommender system using a first garbled circuit based on matrix factorization to obtain a masked item profile for each of a plurality of items in the first set of records, receiving a recommendation request from a requesting user for a particular item, and transferring the masked item profiles to the requesting user, wherein the requesting user evaluates a second record and the masked item profiles by using a second garbled circuit based on ridge regression to obtain the recommendation about the particular item and only known by the requesting user. An equivalent apparatus is configured to perform the method.

Abstract: A method includes: receiving a set of records from a source, wherein each record in the set of records includes a set of tokens, and wherein each record is kept secret from parties other than the source, and evaluating the set of records with a garbled circuit, wherein the output of the garbled circuit is a count based on the set of tokens. An apparatus includes: a processor, that communicates with at least one input/output interface and at least one memory in signal communication with the processor, and wherein the processor is configured to: receive a set of records from a source, wherein each record includes a set of tokens, and wherein each record is kept secret from parties other than the source and evaluate the set of records with a garbled circuit, wherein the output of the garbled circuit is a count based on the set of tokens.

Abstract: A method includes: receiving a set of records, wherein each record in the set of records is received from a respective user and includes a set of tokens and a set of items, and wherein each record is kept secret from parties other than the respective user, receiving a recommendation request from a requesting user for a particular item, evaluating the set of records by using a garbled circuit based on matrix factorization, wherein the output of the garbled circuit includes a masked item profile for a particular item and a masked user profile for the requesting user, receiving an encrypted user profile from the requesting user, generating an encrypted recommendation for the particular item based on the encrypted user profile, and providing the encrypted recommendation to the requesting user, wherein the requesting user decrypts it to obtain the recommendation. An equivalent apparatus is configured to perform the method.

Abstract: A method includes: receiving a set of records from a source, wherein each record in the set of records includes a set of tokens and a set of items, and wherein each record is kept secret from parties other than the source, receiving at least one separate item, and evaluating the set of records and the at least one separate item by using a garbled circuit based on matrix factorization, wherein the output of the garbled circuit includes an item profile for each at least one separate item. An apparatus includes: a processor that communicates with at least one input/output interface, and at least one memory in signal communication with the processor, wherein the processor is configured to perform the method.

Abstract: A method of identifying targeted advertisement to provide advertisement transparency comprises receiving a set of content items having at least one advertisement element embedded within each of the content items and extracting the one or more advertisement element embedded within each of the content items. The method further includes determining a second content item associated with the advertisement element and a semantic category for the first content item and the second content item. The semantic categories of the first content item and the second content item are matched to determine if the advertisement is contextually related with the first content item.

Abstract: A method and apparatus for protecting user privacy in a recommender system are described including determining what information to release to a user for a movie, transmitting the information to the user, accepting obfuscated input from the user and estimating the user's non-private feature vector. Also described are a method and apparatus for protecting user privacy in a recommender system including receiving movie information, accepting a user's movie feedback, accepting user's private information, calculating an obfuscation value and transmitting the obfuscation value.