Abstract: Medication delivery methods, devices and systems are provided. The medication delivery device includes a retractable sheath mechanism connected to a distal end of the device. The retractable sheath mechanism is axially movable between a first position to cover a needle attached to the distal end and a second position to expose the needle with an adjustable protrusion length. The retractable sheath mechanism includes one or more skin sensors disposed on an end surface thereof to detect skin before and during injection. In some cases, a needle carousel is provided to connect to the distal end of the device. The needle carousel supports multiple needle capsules each including a needle.

Abstract: A system uses semantic analysis of text associated with content items to recommend content for display to a user. A subset of representative words from a content description are determined and a content embedding that models the content is generated using a combination of word embeddings associated with each of the representative words. User embeddings are generated using a combination of content embeddings for content that a user has had particular interactions with in a set period of time. Separate user embeddings may be generated to represent user interactions with different categories of content (e.g., travel, photography, apparel, comedy, etc.). The system uses the content embeddings and user embeddings as input to predictive functions which determine a candidate content item that a user is likely to interact with if the candidate content is displayed to the user.

Abstract: Systems and methods for ranking one or more products in online shopping. One or more products are identified based on a search query received from user. The one or more products are ranked based on terms present in the search query. Each of the one or more products has one or more attributes associated with it. An attribute score for each of the one or more products is determined. Further, based on the attribute score, the relevancy of the one or more products is determined. Based on the relevancy, a marginal relevancy score for each of the one or more products is determined. The one or more products are re-ranked based on the marginal relevancy score. The rank of the one or more products can also be modified to optimize revenue generation.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. The online system may include differing levels of information about a selected candidate product in the content item. In various embodiments, the online system determines a level of information about the selected candidate product based on products previously accessed by the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems accessed by the user and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system identifies products for inclusion in the content item and identifies candidate products for inclusion in the content item based on products previously accessed by the user. The online system selects a product of the candidate products based on probabilities of the user accessing content items including different candidate products. The online system includes the content item having information about the selected product in one or more selection processes that select content for presentation to the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. Based on likelihoods of the user accessing content items including different candidate products, the online system selects a candidate product and includes the content item having information about the selected candidate product in one or more selection processes that select content for presentation to the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems the user accessed and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system retrieves products maintained by the online system and identifies candidate products for inclusion in the content item based on relevance of the products to the user. The online system determines probabilities of the user accessing the content item including different candidate products and removes combinations of the content item and candidate products having less than a threshold probability of user interaction. The online system includes one or more combinations of the content item and candidate products in one or more selection processes selecting content for presentation to the user.

Abstract: Systems and methods for ranking one or more products in online shopping. One or more products are identified based on a search query received from user. The one or more products are ranked based on terms present in the search query. Each of the one or more products has one or more attributes associated with it. An attribute score for each of the one or more products is determined. Further, based on the attribute score, the relevancy of the one or more products is determined. Based on the relevancy, a marginal relevancy score for each of the one or more products is determined. The one or more products are re-ranked based on the marginal relevancy score. The rank of the one or more products can also be modified to optimize revenue generation.

Abstract: Systems and methods for ranking one or more products in online shopping. One or more products are identified based on a search query received from user. The one or more products are ranked based on terms present in the search query. Each of the one or more products has one or more attributes associated with it. An attribute score for each of the one or more products is determined. Further, based on the attribute score, the relevancy of the one or more products is determined. Based on the relevancy, a marginal relevancy score for each of the one or more products is determined. The one or more products are re-ranked based on the marginal relevancy score. The rank of the one or more products can also be modified to optimize revenue generation.