Generally, given a non-smoothed document language model P (w|d), which indicates a word distribution for a term w in document d, we attempt to generate a smoothed language model P (w|d + ) that could better estimate the text contents of a document d as d + to avoid zero probabilities for those words not seen in d. Arbitrary assignment of pseudo word counts such as add-λ to every unseen words once was a major improvement for language model smoothing (Chen and Goodman, 1996).
here we propose an optimization framework with regularization for language model smoothing on social networks, using both textual information and the social structure.