wanyu/IteraTeR_v2 huggingface dataset:
Original:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these issues , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Edit intent: clarity
Original:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these problems , we propose to applying meta-graph to HIN-based RSs and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Edit intent: fluency
Original:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) " framework.
Edit intent: fluency
Original:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these problems , we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) ' framework.
Edit intent: fluency
Original:  For the "MF " part, we obtain  user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Revised:  For the MF part, we obtain user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Edit intent: fluency
Original:  For the "MF " part, we obtain  user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Revised:  For the "MF " part, we obtain the user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Edit intent: fluency
Original:  For the "MF " part, we obtain  user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Revised:  For the "MF " part, we obtain user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to get latent features for both users and items.
Edit intent: clarity
Original:  For the "MF " part, we obtain  user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items.
Revised:  For the "MF " part, we obtain user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to obtain latent features for both users and items.
Edit intent: fluency
Original:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other  HIN-based recommending methods.
Revised:  For the FM part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other HIN-based recommending methods.
Edit intent: fluency
Original:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other  HIN-based recommending methods.
Revised:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than FM and other HIN-based recommending methods.
Edit intent: clarity
Original:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other  HIN-based recommending methods.
Revised:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other state-of-the-art HIN-based recommending methods.
Edit intent: fluency
Original:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other  HIN-based recommending methods.
Revised:  For the "FM " part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs . Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other HIN-based recommendation methods.
Edit intent: fluency
Original:  Moreover, side information can alleviate data sparsity and cold start problems facing conventional CF  . However, previous works process different types of information separately, thus losing information that might exist across different types of side information.
Revised:  Moreover, side information can alleviate data sparsity and cold start problems facing conventional CF based methods . However, previous works process different types of information separately, thus losing information that might exist across different types of side information.
Edit intent: clarity
Original:  Since HIN could be a complex graph representing multiple types of relations existing between two entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics driving the similarities between users and items in a HIN, and how to fuse the heterogeneous  information to support recommendation.
Revised:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics driving the similarities between users and items in a HIN, and how to fuse the heterogeneous information to support recommendation.
Edit intent: clarity
Original:  Since HIN could be a complex graph representing multiple types of relations existing between two entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics driving the similarities between users and items in a HIN, and how to fuse the heterogeneous  information to support recommendation.
Revised:  Since HIN could be a complex graph representing multiple types of relations existing between two entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous information to support recommendation.
Edit intent: clarity
Original:  Since HIN could be a complex graph representing multiple types of relations existing between two entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics driving the similarities between users and items in a HIN, and how to fuse the heterogeneous  information to support recommendation.
Revised:  Since HIN could be a complex graph representing multiple types of relations existing between two entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics driving the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation.
Edit intent: fluency
Original:  To address these issues, we propose to apply metagraph to HIN-based RSs and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) ' framework.
Revised:  To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) ' framework.
Edit intent: coherence
Original:  To address these issues, we propose to apply metagraph to HIN-based RSs and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) ' framework.
Revised:  To address these issues, we propose to apply metagraph to HIN-based RSs and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) ' framework.
Edit intent: fluency
Original:  To address these issues, we propose to apply metagraph to HIN-based RSs and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) ' framework.
Revised:  To address these issues, we propose to apply metagraph to HIN-based RSs and solve the information fusion problem with a ` matrix factorization (MF) + factorization machine (FM) " framework.
Edit intent: fluency
Original:  For the FM part, we propose to apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and at the same time identify the useful metagraphs.
Revised:  For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and at the same time identify the useful metagraphs.
Edit intent: clarity
Original:  For the FM part, we propose to apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and at the same time identify the useful metagraphs.
Revised:  For the FM part, we propose to apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and at the same time identify the usefulness of the metagraphs.
Edit intent: clarity
Original: Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs) . In recent years , various types of side information  such as social connections among users and metadata of items  have been introduced into CF and shown to be effective for improving recommendation performance.
Revised: Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors. Recently , various types of side information such as social connections among users and metadata of items have been introduced into CF and shown to be effective for improving recommendation performance.
Edit intent: clarity
Original: Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs) . In recent years , various types of side information  such as social connections among users and metadata of items  have been introduced into CF and shown to be effective for improving recommendation performance.
Revised: Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs) . In recent years , various types of side information such as social connections among users and metadata of items , have been introduced into CF and shown to be effective for improving recommendation performance.
Edit intent: fluency
Original: Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs) . In recent years , various types of side information  such as social connections among users and metadata of items  have been introduced into CF and shown to be effective for improving recommendation performance.
Revised: Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs) . In recent years , various types of side information such as social connections among users and metadata of items have been introduced into CF and shown to be useful for improving recommendation performance.
Edit intent: clarity
Original:  Moreover, side information can alleviate data sparsity and cold start problems facing conventional CF based methods.
Edit intent: coherence
Original:  However, previous works process different types of information separately, thus losing information that might exist across different types of side information.
Revised:  However, previous works process different types of information separately, thus failing to capture the correlations that might exist across different types of side information.
Edit intent: clarity
Original:  In this work, we study the application of Heterogeneous Information Network (HIN) , which offers flexible representation of different types of side information, to enhance CF based recommendation methods.
Revised:  In this work, we study the application of heterogeneous information network (HIN) , which offers flexible representation of different types of side information, to enhance CF based recommendation methods.
Edit intent: fluency
Original:  In this work, we study the application of Heterogeneous Information Network (HIN) , which offers flexible representation of different types of side information, to enhance CF based recommendation methods.
Revised:  In this work, we study the application of Heterogeneous Information Network (HIN) to enhance CF-based recommendation methods.
Edit intent: coherence
Original:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation .
Revised:  However, we face challenging issues in HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation .
Edit intent: coherence
Original:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation .
Revised:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation .
Edit intent: clarity
Original:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation .
Revised:  Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance .
Edit intent: clarity
Original:  To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a `` matrix factorization (MF) + factorization machine (FM) " framework.
Edit intent: fluency
Original:  To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) " framework.
Revised:  To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a " matrix factorization (MF) + factorization machine (FM) '' framework.
Edit intent: fluency
Original:  For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and  at the same time identify the usefulness of the metagraphs.
Revised:  For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and , at the same time identify the usefulness of the metagraphs.
Edit intent: fluency
Original:  Experimental results on two large real-world datasets , i.e., Amazon and Yelp, show that our proposed approach is better than FM and other state-of-the-art HIN-based recommendation methods .
Revised:  Experimental results on two large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art HIN-based recommendation methods .
Edit intent: clarity
Original:  Experimental results on two large real-world datasets , i.e., Amazon and Yelp, show that our proposed approach is better than FM and other state-of-the-art HIN-based recommendation methods .
Revised:  Experimental results on two large real-world datasets , i.e., Amazon and Yelp, show that our proposed approach is better than FM and other state-of-the-art methods in terms of recommendation performance .
Edit intent: clarity
Original: A central question in the mathematical study of evolution is to obtain the fixation probability as function of the initial condition , once we are given a stochastic finite population model without mutations and a (possibly frequency dependent) fitness function .
Revised: The probability that the frequency of a particular trait will eventually become unity, the so-called fixation probability, is a central issue in the study of population evolution. Its computation , once we are given a stochastic finite population model without mutations and a (possibly frequency dependent) fitness function .
Edit intent: clarity
Original:  Despite the fact that the fixation probability is an important macroscopic property of the population, its precise knowledge does not give any useful information about the interaction patterns among individuals in the population.
Revised:  Despite the fact that the fixation probability is an important macroscopic property of the population, its precise knowledge does not give any clear information about the interaction patterns among individuals in the population.
Edit intent: clarity
Original:  Here we work in the inverse problem: From a given fixation pattern  , we obtain a discrete fitness function , which realises this fixation pattern in the Wright-Fisher model.
Revised:  Here we address the inverse problem: From a given fixation pattern , we obtain a discrete fitness function , which realises this fixation pattern in the Wright-Fisher model.
Edit intent: clarity
Original:  This discrete fitness function always exists, but it is not necessary unique.
Revised:  This fitness function always exists, but it is not necessary unique.
Edit intent: clarity
Original:  This discrete fitness function always exists, but it is not necessary unique.
Revised:  This discrete fitness function always exists, but it is not necessarily unique.
Edit intent: fluency
Original:  Subsequently, we show that any such discrete fitness function can be approximated, with arbitrary precision, using d-player game theory, provided d is large enough.
Revised:  Subsequently, we show that any such fitness function can be approximated, with arbitrary precision, using d-player game theory, provided d is large enough.
Edit intent: clarity
Original:  The relative entropy of modified permutation associated with equal states  shows advantages to these time irreversibility parameters and has great improvements to that of the original permutation.
Revised:  The relative entropy of symbolization associated with equal states shows advantages to these time irreversibility parameters and has great improvements to that of the original permutation.
Edit intent: clarity
Original: We present a micro-traffic simulation (named "DeepTraffic ") where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process.
Revised: We present a traffic simulation named DeepTraffic where the planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process.
Edit intent: clarity
Original: We present a micro-traffic simulation (named "DeepTraffic ") where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process.
Revised: We present a micro-traffic simulation (named "DeepTraffic ") where the perception, control, and planning systems for a subset of the vehicles are handled by a single neural network as part of a model-free, off-policy reinforcement learning process.
Edit intent: clarity
Original: We present a micro-traffic simulation (named "DeepTraffic ") where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process.
Revised: We present a micro-traffic simulation (named "DeepTraffic ") where the perception, control, and planning systems for one of the cars are all handled by a neural network as part of a model-free, off-policy reinforcement learning process.
Edit intent: clarity
Original:  The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of DQN variants and hyperparameter configurations through large-scale, open competition.
Revised:  The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of deep Q-learning network variants and hyperparameter configurations through large-scale, open competition.
Edit intent: clarity
Original:  This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space with the objective of their neural network submission to make it onto the top-10 leaderboard .
Revised:  This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space .
Edit intent: coherence