A computer-implemented method for providing a personalized self-support service to a user of an online application coupled with an online community forum, comprising: training a predictive model through a machine training process to learn a relationship between one or more user features and one or more tags based on historical user feature data, wherein training the predictive model comprises: training a model prediction to predict tag probabilities from a set of a user profile and a clickstream, and training a model correlation to establish correlations between tags of each respective question contained in the online community forum, and subtab context of a subtab selection on a web page of the online application and an e-file status that the user has historically been associated with; extracting a plurality of tags from the online community forum containing a plurality of question-answer pairs; creating high-dimensional vectors of each tag extracted from the online community forum based on a contextual relationship of each extracted tag within the online community forum by using a neural probabilistic language model; mapping each respective question of the plurality of question-answer pairs to a set of tags from the plurality of tags extracted from the online community forum; forming average high-dimensional vectors for each respective question of the plurality of question-answer pairs by averaging the high-dimensional vectors of each tag in the set of tags; obtaining user data when an online user takes actions within the online application; predicting tag probabilities of the tags extracted from the online community forum for the online user based on the user data by the predictive model; putting weights on respective high-dimensional vectors of each tag of the tag probabilities by respective tag probabilities; averaging the weighted high-dimensional vectors of tags of the tag probabilities into a user high-dimensional vector; calculating a plurality of similarity metrics, wherein each respective similarity metric measures the similarity between the average high-dimensional vector for a respective question and the user high-dimensional vector; and providing one or more of the plurality of question-answer pairs to the online user based on the plurality of similarity metrics. training a predictive model through a machine training process to learn a relationship between one or more user features and one or more tags based on historical user feature data, wherein training the predictive model comprises: training a model prediction to predict tag probabilities from a set of a user profile and a clickstream, and training a model correlation to establish correlations between tags of each respective question contained in the online community forum, and subtab context of a subtab selection on a web page of the online application and an e-file status that the user has historically been associated with; training a model prediction to predict tag probabilities from a set of a user profile and a clickstream, and training a model correlation to establish correlations between tags of each respective question contained in the online community forum, and subtab context of a subtab selection on a web page of the online application and an e-file status that the user has historically been associated with; extracting a plurality of tags from the online community forum containing a plurality of question-answer pairs; creating high-dimensional vectors of each tag extracted from the online community forum based on a contextual relationship of each extracted tag within the online community forum by using a neural probabilistic language model; mapping each respective question of the plurality of question-answer pairs to a set of tags from the plurality of tags extracted from the online community forum; forming average high-dimensional vectors for each respective question of the plurality of question-answer pairs by averaging the high-dimensional vectors of each tag in the set of tags; obtaining user data when an online user takes actions within the online application; predicting tag probabilities of the tags extracted from the online community forum for the online user based on the user data by the predictive model; putting weights on respective high-dimensional vectors of each tag of the tag probabilities by respective tag probabilities; averaging the weighted high-dimensional vectors of tags of the tag probabilities into a user high-dimensional vector; calculating a plurality of similarity metrics, wherein each respective similarity metric measures the similarity between the average high-dimensional vector for a respective question and the user high-dimensional vector; and providing one or more of the plurality of question-answer pairs to the online user based on the plurality of similarity metrics.