Original: Besides, for IRN-P without any external reward ( d = 0 . 0 ), the depth-2 planner gives better performance than depth-1 and breadth-2 on three measurements (by 2-5%), possibly due to the more predictive representations learned after unsupervised training.
Revised: Besides, for IRN-P without any external reward ( d = 0 . 0 ), the depth-2 planner gives better performance than depth-1 and breadth-2 on three measurements (by 2-5%), possibly due to the more predictive representations learned after unsupervised training.

Original: However, for IRN with purchase reward (semi-supervised learning), the purchase performance cannot be improved using longer imagined trajectories.
Revised: However, for IRN with purchase reward (semi-supervised learning), the purchase performance cannot be improved using longer imagined trajectories.

Original: One possible reason is that two steps of imagination reconstruction is sufﬁcient for learning to predict the future events recursively; the ﬁrst step of IRN learns to capture the difference of adjacent input states, and the second step learns to look ahead the future purchase signal accurately.
Revised: One possible reason is that two steps of imagination reconstruction is sufﬁcient for learning to predict the future events recursively; the ﬁrst step of IRN learns to capture the difference of adjacent input states, and the second step learns to look ahead the future purchase signal accurately.

Original: Robustness to the cold-start scenario We simulate a cold-start scenario using the test set.
Revised: Robustness to the cold-start scenario We simulate a cold-start scenario using the test set.

Original: Speciﬁcally, we use a parameter c to control the number of items in the input state (a set of one-hot vectors of clicked items), i.e., new events will not be added to the input state if the number of items exceeds c , but are still used for evaluations.
Revised: Speciﬁcally, we use a parameter c to control the number of items in the input state (a set of one-hot vectors of clicked items), i.e., new events will not be added to the input state if the number of items exceeds c , but are still used for evaluations.

Original: Figure 4 (a,b) shows the purchase performance w.r.t.
Revised: Figure 4 (a,b) shows the purchase performance w.r.t.

Original: the cold-start parameter c .
Revised: the cold-start parameter c .

Original: We can see that IRN-P outperforms A3C-P and A3C-F over all ranges of c , verifying the effectiveness of imagination reconstruction.
Revised: We can see that IRN-P outperforms A3C-P and A3C-F over all ranges of c , verifying the effectiveness of imagination reconstruction.

Original: In other words, IRN-P can guess what you like (or learn predictive representations) and obtain a better user (or session) proﬁle.
Revised: In other words, IRN-P can guess what you like (or learn predictive representations) and obtain a better user (or session) proﬁle.

Original: Besides, A3C-F achieves slightly better results than A3C-P, which is different from that in Table 1.
Revised: Besides, A3C-F achieves slightly better results than A3C-P, which is different from that in Table 1.

Original: A3C-F that trained with the click reward can preserve the sequential property of sessions, and thus provide auxiliary (implicit) information under the cold-start setting (in the warm-start setting, the agent using more clicked items as input may be biased and thus focuses on next-click prediction).
Revised: A3C-F that trained with the click reward can preserve the sequential property of sessions, and thus provide auxiliary (implicit) information under the cold-start setting (in the warm-start setting, the agent using more clicked items as input may be biased and thus focuses on next-click prediction).

Original: Adaptation to user interest To demonstrate that IRN can improve data efﬁciency and promote quick adaptation to user interest, we create a more realistic scenario for online learning.
Revised: To demonstrate that IRN can improve data efﬁciency and promote quick adaptation to user interest, we create a more realistic scenario for online learning.

Original: Speciﬁcally, the training set is sorted in chronological order, and each event is used only once for training.
Revised: Speciﬁcally, the training set is sorted in chronological order, and each event is used only once for training.

Original: The test set remains unchanged.
Revised: The test set remains unchanged.

Original: Figure 4 (c,d) shows the purchase performance that is evaluated on both the training set ("-tr", averaged over a batch of training purchases) and the test set ("-te", averaged over all test purchases); for a given purchase event in the training set, the model ﬁrst checks its rank and then uses it for an incremental update (measuring the short-term interest).
Revised: Figure 4 (c,d) shows the purchase performance that is evaluated on both the training set ("-tr", averaged over a batch of training purchases) and the test set ("-te", averaged over all test purchases); for a given purchase event in the training set, the model ﬁrst checks its rank and then uses it for an incremental update (measuring the short-term interest).

Original: We can see that, IRN-P promotes quick adaptation to user interest (after 2000 iterations) compared to A3C-P and A3C-F on the two datasets, and IRN-P-te shows improved data efﬁciency and purchase performance compared to A3C-F-te and A3C-P-te after online learning.
Revised: We can see that, IRN-P promotes quick adaptation to user interest (after 2000 iterations) compared to A3C-P and A3C-F on the two datasets, and IRN-P-te shows improved data efﬁciency and purchase performance compared to A3C-F-te and A3C-P-te after online learning.

Original: Different from IRN-P, A3C-F and A3C-P perform poorly on the test set (compared to that of the last training batch); this highlights the importance of most recent events and demonstrates that IRN-P can capture user’s long-term interest ahead of time.[Table]
Revised: Different from IRN-P, A3C-F and A3C-P perform poorly on the test set (compared to that of the last training batch); this highlights the importance of most recent events and demonstrates that IRN-P can capture user’s long-term interest ahead of time.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper we propose the IRN architecture for session-based recommendation, which is inspired by the theories of cognition science.
Revised: In this paper, we propose the IRN architecture for session-based recommendation, which is inspired by the theories of cognition science.

Original: IRN can be regarded as a combination of model-based planning and self-supervised reinforcement learning, which employs a self-supervised reconstruction method for predictive learning, using the imagined trajectories generated by the internal model.
Revised: IRN can be regarded as a combination of model-based planning and self-supervised reinforcement learning, which employs a self-supervised reconstruction method for predictive learning, using the imagined trajectories generated by the internal model.

Original: We conducted experiments to study the impacts of difference components under different scenarios, verifying the effectiveness of our IRN architecture.
Revised: We conducted experiments to study the impacts of difference components under different scenarios, verifying the effectiveness of our IRN architecture.

Original: We believe this kind of approaches has the potential to make a shift in the way we use recommender systems
Revised: We believe this kind of approaches has the potential to make a shift in the way we use recommender systems.