Recommender systems are intelligent E-commerce applications. They assist users in their information-seeking tasks by suggesting items (products, services, or information) that best fit their needs and preferences. Recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains including movies, music, books, search queries, and social tags (Resnick and Varian, 1997; Ricci et al., 2011). Typically, a recommendation procedure can be modeled as interactions between users and recommender agent (RA). It consists of two phases: 1) user model construction and 2) recommendation generation (Mahmood and Ricci, 2007). During the interaction, the recommender agent builds a user model to learn users’ preferences based on users’ information or feedback. Then, the recommender agent generates a list of items that best match users’ preferences.
Most existing recommender systems including collaborative filtering, content-based and learning-to-rank consider the recommendation procedure as a static process and make recommendations following a fixed greedy strategy. However, these approaches may fail given the dynamic nature of the users’ preferences. Furthermore, the majority of existing recommender systems are designed to maximize the immediate (short-term) reward of recommendations, i.e., to make users purchase the recommended items, while completely overlooking whether these recommended items will lead to more profitable (long-term) rewards in the future (Shani et al., 2005).
In this paper, we consider the recommendation procedure as sequential interactions between users and recommender agent; and leverage Reinforcement Learning (RL) to automatically learn the optimal recommendation strategies. Recommender systems based on reinforcement learning have two advantages. First, they are able to continuously update their try-and-error strategies during the interactions, until the system converges to the optimal strategy that generates recommendations best fitting their users’ dynamic preferences. Second, the models in the system are trained via estimating the present value with delayed rewards under current states and actions. The optimal strategy is made by maximizing the expected long-term cumulative reward from users. Therefore the system could identify items with small immediate rewards but making big contributions to the rewards for future recommendations.
modeled the recommender system as a MDP process and estimated the transition probability and then the Q-value table. However, these methods may become inflexible with the increasing number of items for recommendations. This prevents them from being adopted in e-commence recommender systems. Thus, we leverage Deep Q-Network (DQN), an (adapted) artificial neural network, as a non-linear approximator to estimate the action-value function in RL. This model-free reinforcement learning method does not estimate the transition probability and not store the Q-value table. This makes it flexible to support huge amount of items in recommender systems. It can also enrich the system’s generalization compared to traditional approaches that estimate action-value function separately for each sequence.
When we design recommender systems, positive feedback(clicked /ordered feedback) represents the users’ preference and thus is the most important information to make recommendations. In reality, users also skip some recommended items during the recommendation procedure. These skipped items influence user’s click/order behaviors (Dupret and Piwowarski, 2008), which can help us gain better understandings about users’ preferences. Hence, it is necessary to incorporate such negative feedback. However, the number of skipped items (or negative feedback) is typically far larger than that of positive ones. Hence, it is challenging to capture both positive and negative feedback since positive feedback could be buried by negative one. In this paper, we propose a framework DEERS to model positive and negative feedback simultaneously. As shown in Figure 1, when items are skipped by a user, traditional recommender systems do not update their strategies, and still recommend similar items; while the proposed framework can capture these negative feedback and update the strategy for recommendations. We summarize our major contributions as follows:
We identify the importance of negative feedback from the recommendation procedure and provide a principled approach to capture it for recommendations;
We propose a deep reinforcement learning based framework DEERS, which can automatically learn the optimal recommendation strategies by incorporating positive and negative feedback;
We demonstrate the effectiveness of the proposed framework in real-world e-commerce data and validate the importance of negative feedback in accurate recommendations.
The rest of this paper is organized as follows. In Section 2, we formally define the problem of recommender system via reinforcement learning. In Section 3, we provide approaches to model the recommending procedure as sequential user-agent interactions and introduce details about employing deep reinforcement learning to automatically learn the optimal recommendation strategies via offline-training. Section 4 carries out experiments based on real-word offline users’ behavior log and presents experimental results. Section 5 briefly reviews related work. Finally, Section 6 concludes this work and discusses our future work.
2. Problem Statement
We study the recommendation task in which a recommender agent (RA) interacts with environment (or users) by sequentially choosing recommendation items over a sequence of time steps, so as to maximize its cumulative reward. We model this problem as a Markov Decision Process (MDP), which includes a sequence of states, actions and rewards. More formally, MDP consists of a tuple of five elements as follows:
State space : A state is defined as the browsing history of a user before time . The items in are sorted in chronological order.
Action space : The action of RA is to recommend items to a user at time . Without the loss of generality, we assume that the agent only recommends one item to the user each time. Note that it is straightforward to extend it with recommending multiple items.
Reward : After the RA taking an action at the state , i.e., recommending an item to a user, the user browses this item and provides her feedback. She can skip (not click), click, or order this item, and the RA receives immediate reward according to the user’s feedback.
Transition probability : Transition probability defines the probability of state transition from to when RA takes action . We assume that the MDP satisfies .
Discount factor : defines the discount factor when we measure the present value of future reward. In particular, when , RA only considers the immediate reward. In other words, when , all future rewards can be counted fully into that of the current action.
Figure 2 illustrates the agent-user interactions in MDP. With the notations and definitions above, the problem of item recommendation can be formally defined as follows: Given the historical MDP, i.e., , the goal is to find a recommendation policy , which can maximize the cumulative reward for the recommender system.
3. The Proposed Framework based on Deep Reinforcement Learning with Negative Feedbacks
Negative feedback dominates users’ feedback to items and positive feedback could be buried by negative one if we aim to capture them simultaneously. Thus in this section, we first propose a basic model that only considers positive feedback (clicked/ordered items) into the state. We build the deep reinforcement learning framework under this setting. After that, we consider negative feedback (skipped items) in the state space and redesign the deep architecture. Finally, we discuss how to train the framework via offline users’ behavior log and how to utilize the framework for item recommendations.
3.1. The Basic DQN Model
The positive items represent the key information about users’ preference, i.e., which items the users prefer to. A good recommender system should recommend the items users prefer the most. Thus, we only consider the positive feedback into the state in the basic model. More formally, we redefine the state and transition process for the basic model below:
State : State is defined as the previous items that a user clicked/ordered recently. The items in state are sorted in chronological order.
Transition from to : When RA recommends item at state to a user, the user may skip, click, or order the item. If the user skips the item, then the next state ; while if the user clicks/orders the item, then the next state .
Note that in reality, using discrete indexes to denote items is not sufficient since we cannot know the relations between different items only from indexes. One common way is to use extra information to represent items. For instance, we can use the attribute information like brand, price, sale per month, etc. Instead of extra item information, our model uses the RA/user interaction information, i.e., users’ browsing history. We treat each item as a word and the clicked items in one recommendation session as a sentence. Then, we can obtain dense and low-dimensional vector representations for items by word embedding(Levy and Goldberg, 2014).
As shown is Figure 2, by interacting with the environment (users), recommender agent takes actions (recommends items) to users in such a way that maximizes the expected return, which includes the delayed rewards. We follow the standard assumption that delayed rewards are discounted by a factor of per time-step, and define the action-value function as the expected return based on state and action . The optimal action-value function , which has the maximum expected return achievable by the optimal policy, should follow the Bellman equation (Bellman, 2013) as:
In real recommender systems, the state and action spaces are enormous, thus estimating by using the Bellman equation for each state-action pair is infeasible. In addition, many state-action pairs may not appear in the real trace such that it is hard to update their values. Therefore, it is more flexible and practical to use an approximator function to estimate the action-value function, i.e., . In practice, the action-value function is usually highly nonlinear. Deep neural networks are known as excellent approximators for non-linear functions. In this paper, We refer to a neural network function approximator with parameters as a -network. A
-network can be trained by minimizing a sequence of loss functionsas:
where is the target for the current iteration. The parameters from the previous iteration are fixed when optimizing the loss function . The derivatives of loss function with respective to parameters are presented as follows:
3.2. The Proposed DEERS Framework
As previously discussed, the items that users skipped can also indicate users’ preferences, i.e., what users may not like. However, the system with only positive items will not change its state or update its strategy when users skip the recommended items. Thus, the state should not only contain positive items that user clicked or ordered, but also incorporate negative (skipped) items. To integrate negative items into the model, we redefine state and transition process as follows:
State : is a state, where is defined as the previous items that a user clicked or ordered recently, and is the previous items that the user skipped recently. The items in and are processed in chronological order.
Transition from to : When RA recommends item at state to a user, if users skip the recommended item, we keep and update . If users click or order the recommended item, update , while keeping . Then set .
We can see from the new definition that no matter users accept or skip recommendations, the new system will incorporate the feedback for next recommendations. Also it can distinguish positive and negative feedback with and .
3.2.1. The Architecture of DEERS Framework
In this approach, we concatenate positive state and a recommended item as positive input (positive signals), while concatenating negative state and the recommended item as the negative input (negative signals). Rather than following the basic reinforcement learning architecture in Figure 3, we construct DQN in a novel way. Figure 4 illustrates our new DQN architecture. Instead of just concatenating clicked/ordered items as positive state
, we introduce a RNN with Gated Recurrent Units (GRU) to capture users’ sequential preference as positive state:
where GRU utilizes an update gate to generate a new state and a reset gate to control the input of the former state . The inputs of GRU are the embeddings of user’s recently clicked /ordered items , while we use the output (final hidden state ) as the representation of the positive state, i.e., . We obtain negative state
following a similar way. Here we leverage GRU rather than Long Short-Term Memory (LSTM) because that GRU outperforms LSTM for capturing users’ sequential behaviors in recommendation task(Hidasi et al., 2015).
As shown in Figure 4, we feed positive input (positive signals) and negative input (negative signals) separately in the input layer. Also, different from traditional fully connected layers, we separate the first few hidden layers for positive input and negative input. The intuition behind this architecture is to recommend an item that is similar to the clicked/ordered items (left part), while dissimilar to the skipped items (right part). This architecture can assist DQN to capture distinct contributions of the positive and negative feedback to recommendations.
Then, the loss function for an iteration of the new deep -network training can be rewritten as:
where is the target of current iteration. Then, the gradient of loss function with respect to the can be computed as:
To decrease the computational cost of Equation (9), we adopt item recalling mechanism to reduce the number of relevant items 111 In general, user’s current preference should be related to user’s previous clicked/ordered items (say ). Thus for each item in , we collect a number of most similar items from the whole item space, and combine all collected items as the item space for current recommendation session..
3.2.2. The Pairwise Regularization Term
With deep investigations on the users’ logs, we found that in most recommendation sessions, the RA recommends some items belong to the same category (e.g. telephone), while users click/order a part of them and skip others. We illustrate a real example of a recommendation session in Table 1, in which three categories of items are recommended to user, and each time the RA recommends one item to the user. For category B, we can observe that the user clicked item while skipped item , which indicates the partial order of user’s preference over these two items in category B. This partial order naturally inspires us maximizing the difference of Q-values between and . At time 2, we name as the competitor item of . Sometimes, one item could have multiple “competitor” items; thus we select the item at the closest time as the “competitor” item. For example, at time 3, ’s competitor item is rather than . Overall, we select one target item’s competitor item according to three requirements: 1) the “competitor” item belongs to the same category with the target item; 2) user gives different types of feedback to the “competitor” item and the target item; and 3) the “competitor” item is at the closest time to the target item.
To maximize the difference of Q-values between target and competitor items, we add a regularization term to Equation 8:
where is the target for the iteration. The second term aims to maximize the difference of Q-values between target item and competitor item at state , which is controlled by a non-negative parameter . Note that since user’s preference is relatively stable during a short time slot (Wu et al., 2017), we assume that user will give same feedback to at state . For example, if RA recommends item at state , the user will still skip . The gradient of loss function can be computed as:
3.3. Off-policy Training Task
With the proposed deep reinforcement learning framework, we can train the parameters in models and then do the test work. We train the proposed model based on users’ offline log, which records the interaction history between RA’s policy and users’ feedback. RA takes the action based on the off-policy and obtain the feedback from the offline log. We present our off-policy training algorithm in details shown in Algorithm 1. Note that our algorithm is model-free: it solves the reinforcement learning task directly using samples from the environment, without explicitly constructing an estimate of the environment.
In each iteration on a training recommendation session, there are two stages. For storing transitions stage: given the state , the recommender agent first recommends an item from a fixed off-policy (line 7), which follows a standard off-policy way (Degris et al., 2012); then the agent observes the reward from users (line 8) and updates the state to (line 9) and try to find the competitor item (line 10); and finally the recommender agent stores transitions into replay memory (line 11). For model training stage: the recommender agent samples minibatch of transitions from replay memory (line 12), and then updates the parameters according to Equation (9) or Equation (11) (lines 13-18).
In the algorithm, we introduce widely used techniques to train our framework. For example, we utilize a technique known as experience replay (Lin, 1993) (lines 1,11,12), and introduce separated evaluation and target networks (Mnih et al., 2013), which can help smooth the learning and avoid the divergence of parameters. Moreover, we leverage prioritized sampling strategy (Moore and Atkeson, 1993) to assist the framework learning from the most important historical transitions.
3.4. Offline Test
In the previous subsection, we have discussed how to train a DQN-based recommender model. Now we formally present the offline test of our proposed framework DEERS.
The intuition of our offline test method is that, for a given recommendation session, the recommender agent reranks the items in this session according to the items’ Q-value calculated by the trained DQN, and recommends the item with maximal Q-value to the user. Then the recommender agent observes the reward from users and updates the state. The reason why recommender agent only reranks items in this session rather than items in the whole item space is that for the historical offline dataset, we only have the ground truth rewards of the existing items in this session. The offline test algorithm in details is presented in Algorithm 2.
In each iteration of a test recommendation session , given the state (line 2), the recommender agent recommends an item with maximal Q-value calculated by the trained DQN (lines 3-4), then observes the reward from users (line 5) and updates the state to (line 6), and finally it removes item from (line 7). Without the loss of generality, the RA recommends one item to the user each time, while it is straightforward to extend it by recommending multiple items.
3.5. Online Test
We also do online test on a simulated online environment. The simulated online environment is also trained on users’ logs, but not on the same data for training the DEERS framework. The simulator has the similar architecture with DEERS, while the output layer is a softmax layer that predicts the immediate feedback according to current stateand a recommended item . We test the simulator on users’ logs(not the data for training the DEERS framework and simulator), and experimental results demonstrate that the simulated online environment has overall 90% precision for immediate feedback prediction task. This result suggests that the simulator can accurately simulate the real online environment and predict the online rewards, which enables us to test our model on it.
When test DEERS framework on the well trained simulator, we can feed current state and a recommended item into the simulator, and receive immediate reward from it. The online test algorithm in details is presented in Algorithm 3. Note that we can control the length of the session (the value ) manually.
In each iteration of a test recommendation session , given the state (line 5), the RA recommends an item following policy (line 6), which is obtained in model training stage. Then the RA feed and into the simulated online environment and observes the reward (line 7) and updates the state to (line 8).
In this section, we conduct extensive experiments with a dataset from a real e-commerce site to evaluate the effectiveness of the proposed framework. We mainly focus on two questions: (1) how the proposed framework performs compared to representative baselines; and (2) how the negative feedback (skipped items) contribute to the performance. We first introduce experimental settings. Then we seek answers to the above two questions. Finally, we study the impact of important parameters on the performance of the proposed framework.
4.1. Experimental Settings
We evaluate our method on a dataset of September, 2017 from JD.com. We collect 1,000,000 recommendation sessions (9,136,976 items) in temporal order, and use first 70% as training set and other 30% as test set. For a given session, the initial state is collected from the previous sessions of the user. In this paper, we leverage previously clicked/ordered items as positive state and previously skipped items as negative state. The reward of skipped/clicked/ordered items are empirically set as 0, 1, and 5, respectively. The dimension of the embedding of items is 50, and we set the discounted factor . For the parameters of the proposed framework such as and , we select them via cross-validation. Correspondingly, we also do parameter-tuning for baselines for a fair comparison. We will discuss more details about parameter selection for the proposed framework in the following subsections.
For the architecture of Deep Q-network, we leverage a 5-layer neural network, in which the first 3 layers are separated for positive and negative signals, and the last 2 layers connects both positive and negative signals, and outputs the Q-value of a given state and action.
As we consider our offline test task as a reranking task, we select MAP (Turpin and Scholer, 2006) and NDCG@40 (Järvelin and Kekäläinen, 2002) as the metrics to evaluate the performance. The difference of ours from traditional Learn-to-Rank methods is that we rerank both clicked and ordered items together, and set them by different rewards, rather than only rerank clicked items as that in Learn-to-Rank problems. For online test, we leverage the accumulated rewards in the session as the metric.
4.2. Performance Comparison for Offline Test
We compare the proposed framework with the following representative baseline methods:
CF: Collaborative filtering(Breese et al., 1998) is a method of making automatic predictions about the interests of a user by collecting preference information from many users, which is based on the hypothesis that people often get the best recommendations from someone with similar tastes to themselves.
GRU: This baseline utilizes the Gated Recurrent Units (GRU) to predict what user will click/order next based on the clicking/ordering histories. To make comparison fair, it also keeps previous clicked/ordered items as states.
The results are shown in Figure 5. We make following observations:
Figure 5 (a) illustrates the training process of DEERS. We can observe that the framework approaches to convergence when the model are trained by 500,000 sessions. The fluctuation of the curve causes by the parameter replacement from evaluation DQN to target DQN.
CF and FM achieve worse performance than GRU, DQN and DEERS, since CF and FM ignore the temporal sequence of the users’ browsing history, while GRU can capture the temporal sequence, and DEERS-p and DEERS are able to continuously update their strategies during the interactions.
GRU achieves worse performance than DEERS-p, since we design GRU to maximize the immediate reward for recommendations, while DEERS-p is designed to achieve the trade-off between short-term and long-term rewards. This result suggests that introducing reinforcement learning can improve the performance of recommendations.
DEERS performs better than DEERS-p because we integrate both positive and negative items (or feedback) into DEERS, while DEERS-p is trained only based on positive items. This result indicates that negative feedback can also influence the decision making process of users, and integrating them into the model can lead to accurate recommendations.
To sum up, we can draw answers to the two questions: (1) the proposed framework outperforms representative baselines; and (2) negative feedback can help the recommendation performance.
4.3. Performance Comparison for Online Test
We do online test on the aforementioned simulated online environment, and compare with DEERS with GRU and several variants of DEERS. Note that we do not include CF and FM baselines as offline test since CF and FM are not applicable to the simulated online environment.
GRU: The same GRU as in the above subsection.
DEERS-p: The same DEERS-p as in the above subsection.
DEERS-f: This variant is a traditional 5-layer DQN where all layers are fully connected. Note that the state is also captured by GRU.
DEERS-t: In this variant, we remove GRU units and just concatenate the previous clicked/ordered items as positive state and previous skipped items as negative state.
DEERS-r: This variant is to evaluate the performance of pairwise regularization term, so we set to eliminate the pairwise regularization term.
As the test stage is based on the simulator, we can artificially control the length of recommendation sessions to study the performance in short and long sessions. We define short sessions with 100 recommended items, while long sessions with 300 recommended items. The results are shown in Figure 6. It can be observed:
DEERS outperforms DEERS-f, which demonstrates that the new architecture of DQN can indeed improve the recommendation performance.
An interesting discovery is that DEERS-f achieves even worse performance than DEERS-p, which is trained only based on positive feedback. This result indicates that a full connected DQN cannot detect the differences between positive and negative items, and simply concatenating positive and negative feedback as input will reduce the performance of traditional DQN. Thus redesigning DQN architecture is a necessary.
In short recommendation sessions, GRU and DEERS-p achieve comparable performance. In other words, GRU and reinforcement learning models like DEERS-p can both recommend proper items matching users’ short-term interests.
In long recommendation sessions, DEERS-p outperforms GRU significantly, because GRU is designed to maximize the immediate reward for recommendations, while reinforcement learning models like DEERS-p are designed to achieve the trade-off between short-term and long-term rewards.
DEERS outperforms DEERS-t and DEERS-r, which suggests that introducing GRU to capture users’ sequential preference and introducing pairwise regularization term can improve the performance of recommendations.
In summary, appropriately redesigning DQN architecture to incorporate negative feedback, leveraging GRU to capture users’ dynamic preference and introducing pairwise regularization term can boost the recommendation performance.
4.4. Parameter Sensitivity Analysis
Our method has two key parameters, i.e., that controls the pairwise regularization term and that controls the length of state. To study the impact of these parameters, we investigate how the proposed framework DEERS works with the changes of one parameter, while fixing other parameters.
Figure 7 (a) shows the parameter sensitivity of in offline recommendation task. The performance for recommendation achieves the peak when . In other words, the the pairwise regularization term indeed improves the performance of the model; however, it is less important than the first term in Equation 10.
Figure 7 (b) demonstrates the parameter sensitivity of in offline recommendation task. We find that with the increase of , the performance improves, and the performance is more sensitive with positive items. In other words, user’s decision mainly depends on the items she/he clicked or ordered, but the skipped items also influence the decision making process.
5. Related Work
In this section, we briefly review works related to our study. In general, the related work can be mainly grouped into the following categories.
The first category related to this paper is traditional recommendation techniques. Recommender systems assist users by supplying a list of items that might interest users. Efforts have been made on offering meaningful recommendations to users. Collaborative filtering (Linden et al., 2003) is the most successful and the most widely used technique, which is based on the hypothesis that people often get the best recommendations from someone with similar tastes to themselves (Breese et al., 1998). Another common approach is content-based filtering (Mooney and Roy, 2000), which tries to recommend items with similar properties to those that a user ordered in the past. Knowledge-based systems (Akerkar and Sajja, 2010) recommend items based on specific domain knowledge about how certain item features meet users needs and preferences and how the item is useful for the user. Hybrid recommender systems are based on the combination of the above mentioned two or more types of techniques (Burke, 2002)
. The other topic closely related to this category is deep learning based recommender system, which is able to effectively capture the non-linear and non-trivial user-item relationships, and enables the codification of more complex abstractions as data representations in the higher layers(Zhang et al., 2017). For instance, Nguyen et al. (Nguyen et al., 2017)
proposed a personalized tag recommender system based on CNN. It utilizes constitutional and max-pooling layer to get visual features from patches of images. Wu et al.(Wu et al., 2016) designed a session-based recommendation model for real-world e-commerce website. It utilizes the basic RNN to predict what user will buy next based on the click histories. This method helps balance the trade off between computation costs and prediction accuracy.
The second category is about reinforcement learning for recommendations, which is different with the traditional item recommendations. In this paper, we consider the recommending procedure as sequential interactions between users and recommender agent; and leverage reinforcement learning to automatically learn the optimal recommendation strategies. Indeed, reinforcement learning have been widely examined in recommendation field. The MDP-Based CF model can be viewed as approximating a partial observable MDP (POMDP) by using a finite rather than unbounded window of past history to define the current state (Shani et al., 2005). To reduce the high computational and representational complexity of POMDP, three strategies have been developed: value function approximation (Hauskrecht, 1997), policy based optimization (Ng and Jordan, 2000; Poupart and Boutilier, 2005), and stochastic sampling (Kearns et al., 2002). Furthermore, Mahmood et al. (Mahmood and Ricci, 2009) adopted the reinforcement learning technique to observe the responses of users in a conversational recommender, with the aim to maximize a numerical cumulative reward function modeling the benefit that users get from each recommendation session. Taghipour et al. (Taghipour et al., 2007; Taghipour and Kardan, 2008) modeled web page recommendation as a Q-Learning problem and learned to make recommendations from web usage data as the actions rather than discovering explicit patterns from the data. The system inherits the intrinsic characteristic of reinforcement learning which is in a constant learning process. Sunehag et al. (Sunehag et al., 2015) introduced agents that successfully address sequential decision problems with high-dimensional combinatorial state and action spaces. Zhao et al. (Zhao et al., 2017, 2018) propose a novel page-wise recommendation framework based on deep reinforcement learning, which can optimize a page of items with proper display based on real-time feedback from users.
In this paper, we propose a novel framework DEERS, which models the recommendation session as a Markov Decision Process and leverages Reinforcement Learning to automatically learn the optimal recommendation strategies. Reinforcement learning based recommender systems have two advantages: (1) they can continuously update strategies during the interactions, and (2) they are able to learn a strategy that maximizes the long-term cumulative reward from users. Different from previous work, we leverage Deep Q-Network and integrate skipped items (negative feedback) into reinforcement learning based recommendation strategies. Note that we design a novel architecture of DQN to capture both positive and negative feedback simultaneously. We evaluate our framework with extensive experiments based on data from a real e-commerce site. The results show that (1) our framework can significantly improve the recommendation performance; and (2) skipped items (negative feedback) can assist item recommendation.
There are several interesting research directions. First, in addition to positional order of items we used in this work, we would like to investigate more orders like temporal order. Second, we would like to validate with more agent-user interaction patterns, e.g., storing items into shopping cart, and investigate how to model them mathematically for recommendations. Finally, the items skipped by users may not be caused by users disliking them, but just not preferring as more as the items clicked/ordered or not viewing them in details at all. The week/wrong negative feedback may not improve or even reduce the performance when we consider the negative feedback. To capture stronger negative feedback, more information like dwell time can be recorded in users’ behavior log and used in our framework.
This material is based upon work supported by, or in part by, the National Science Foundation (NSF) under grant number IIS-1714741 and IIS-1715940.
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