Abstract: In one embodiment, a method includes identifying a particular item of interest of a user; determining a grouping of a number of items that are similar to the particular item; automatically creating a new video by combining the particular item and the respective number of grouped items that are similar to the particular item in accordance with a video-creation template; and providing the created video for display on a client device of the user.

Abstract: In one embodiment, a method includes identifying a particular item of interest of a user; determining a grouping of a number of items that are similar to the particular item; automatically creating a new video by combining the particular item and the respective number of grouped items that are similar to the particular item in accordance with a video-creation template; and providing the created video for display on a client device of the user.

Abstract: In one embodiment, a method includes accessing data of a number of items from a third-party computing system; identifying a subset of the number of items based on a value of a ranking model for each identified item being higher than a pre-determined threshold value; for each identified item of the subset, determining a number of similar items from the number of items; receiving a template for a video from the third-party computing system; for each of the identified items, generating a video incorporating the identified item and the respective number of similar items in accordance with the template; caching the generated videos; and providing for display on a client device of a user, a particular one of the videos selected based on information of the user.

Abstract: An online system uses classifiers to predict the socioeconomic group of users of the online system. The classifiers use models that are trained using features based on global information about a population of users such as demographic information, device ownership, internet usage, household data, and socioeconomic status. The global information can be aggregated from market research questionnaires and provided to the online system. The classifiers input information about a user and output a probability that the user belongs to a given socioeconomic group. The input information is based on a user profile on the online system associated with the user as well as actions performed by the user on the online system. Thus, the online system can predict the user's socioeconomic group without using the user's income information. The online system can generate content for presentation to the user based on the predicted socioeconomic group.

Abstract: An online system uses classifiers to predict the socioeconomic group of users of the online system. The classifiers use models that are trained using features based on global information about a population of users such as demographic information, device ownership, internet usage, household data, and socioeconomic status. The global information can be aggregated from market research questionnaires and provided to the online system. The classifiers input information about a user and output a probability that the user belongs to a given socioeconomic group. The input information is based on a user profile on the online system associated with the user as well as actions performed by the user on the online system. Thus, the online system can predict the user's socioeconomic group without using the user's income information. The online system can generate content for presentation to the user based on the predicted socioeconomic group.