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In the implicit feedback recommendation, incorporating short-term preferenceinto recommender systems has attracted increasing attention in recent years.However, unexpected behaviors in historical interactions like clicking someitems by accident don't well reflect users' inherent preferences. Existingstudies fail to model the effects of unexpected behaviors, thus achieveinferior recommendation performance. In this paper, we propose aMulti-Preferences Model (MPM) to eliminate the effects of unexpected behaviors.MPM first extracts the users' instant preferences from their recent historicalinteractions by a fine-grained preference module. Then an unexpected-behaviorsdetector is trained to judge whether these instant preferences are biased byunexpected behaviors. We also integrate user's general preference in MPM.Finally, an output module is performed to eliminate the effects of unexpectedbehaviors and integrates all the information to make a final recommendation. Weconduct extensive experiments on two datasets of a movie and an e-retailing,demonstrating significant improvements in our model over the state-of-the-artmethods. The experimental results show that MPM gets a massive improvement inHR@10 and NDCG@10, which relatively increased by 3.643% and 4.107% compare withAttRec model on average. We publish our code athttps://github.com/chenjie04/MPM/.

Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors