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