Abstract: A method of managing an online support group to increase the odds that users will attain their wellness goals is disclosed. A request from a user to join a support group is received. A preference of the user with respect to a type of the support group is received. The support group is selected from a plurality of support groups based on the preference of the user and a preference of a member of the support group. Based on an acceptance by the user of an option to join the support group, the plurality of support groups is reorganized.

Abstract: A method of managing an online support group to increase the odds that users will attain their wellness goals is disclosed. A request from a user to join a support group is received. A preference of the user with respect to a type of the support group is received. The support group is selected from a plurality of support groups based on the preference of the user and a preference of a member of the support group. Based on an acceptance by the user of an option to join the support group, the plurality of support groups is reorganized.

Abstract: A method of managing an online support group to increase the odds that users will attain their wellness goals is disclosed. A request from a user to join a support group is received. A preference of the user with respect to a type of the support group is received. The support group is selected from a plurality of support groups based on the preference of the user and a preference of a member of the support group. Based on an acceptance by the user of an option to join the support group, the plurality of support groups is reorganized.

Abstract: A method of predicting food items consumed by a user of a food-logging application is disclosed. Loggings of consumptions of food items are received. A predictive model is generated based on the received loggings. The predictive model generates a prediction of one or more additional food items that a target user will consume or is likely to have consumed (e.g., at a particular time). The prediction is generated based on an application of the predictive model to one or more data items (e.g., data items streaming into the system in real time from the target user or other users that are relevant to food consumptions by the target user). The prediction of the consumption of the one or more additional food items by the user may then be communicated for presentation to the target user in a user interface.