Multiple Monotonic Behavior Chains for Implicit Feedback Data

In the past few decades, "explicit" and "implicit" feedback information in recommendation systems have been researched as two separate areas. But, in the real world, user behaviors contain a series of explicit and implicit information, ranging from clicks, collections to purchases and ratings. It is still an unsolved problem of how to effectively use abundant implicit signals to help predict more accurate explicit signals and thus generate more appropriate recommendations. Therefore, we propose a multiple monotonic behavior chains model (MC-Rec), which can simulate the full range of interactions. The Adaboost algorithm is used to set the weights of each behavior chain, and the monotony of the behavior chain is lucubrated. The experimental results are evaluated in three real data sets, and demonstrate that MC-Rec model can effectively improve the prediction accuracy compared with the state-of-the-art baselines.