15. An apparatus, comprising: a processor; a memory; an event-prediction module to compute a first set of activity probabilities based on contextual information for a first set of historical activities and an activity-prediction model for a target activity, wherein an activity probability indicates a likelihood that the target activity has occurred; a threshold-generating module to generate a first set of probability thresholds; a threshold-scoring module to: compare a first set of probability thresholds with the first set of activity probabilities to determine a prediction success rate for each probability threshold; compute a utility score for each probability threshold based on the prediction success rates and a utility function, wherein the utility function has the form: 
 U ( P th )= U 1 (TP)+ U 2 (FP)+ U 3 (FN)+ U 4 (TN); wherein P th is the threshold value used to determine whether a historical activity matches the target activity, wherein U 1 (TP) and U 4 (TN) compute a benefit of making a recommendation based on predicting a true positive and a true negative, respectively, and wherein U 2 (FP) and U 3 (FN) compute a cost of making a recommendation based on predicting a false positive and a false negative, respectively; and select a first probability threshold whose utility score is greater than or equal to a baseline utility score and other utility scores of the first set of thresholds; and a threshold-assigning module to assign the first probability threshold to the activity-prediction model.
15. An apparatus, comprising: a processor; a memory; an event-prediction module to compute a first set of activity probabilities based on contextual information for a first set of historical activities and an activity-prediction model for a target activity, wherein an activity probability indicates a likelihood that the target activity has occurred; a threshold-generating module to generate a first set of probability thresholds; a threshold-scoring module to: compare a first set of probability thresholds with the first set of activity probabilities to determine a prediction success rate for each probability threshold; compute a utility score for each probability threshold based on the prediction success rates and a utility function, wherein the utility function has the form: 
 U ( P th )= U 1 (TP)+ U 2 (FP)+ U 3 (FN)+ U 4 (TN); wherein P th is the threshold value used to determine whether a historical activity matches the target activity, wherein U 1 (TP) and U 4 (TN) compute a benefit of making a recommendation based on predicting a true positive and a true negative, respectively, and wherein U 2 (FP) and U 3 (FN) compute a cost of making a recommendation based on predicting a false positive and a false negative, respectively; and select a first probability threshold whose utility score is greater than or equal to a baseline utility score and other utility scores of the first set of thresholds; and a threshold-assigning module to assign the first probability threshold to the activity-prediction model. 19. The apparatus of claim 15 , further comprising a baseline-scoring module to compute the baseline utility score by using the utility function to compute one or more of: U(Random); U(Popularity); and U(Cautious); wherein U(Random) computes the utility score using a plurality of randomly-selected historical activities; wherein U(Popularity) computes the utility score using a plurality of most-popular or least-popular historical activities; and wherein U(Cautious) computes the utility score when no historical activities are selected.