Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback (2022)

Abstract: News recommendation is different from movie or e-commercial recommendation as people usually do not grade the news. Therefore, user feedback for news is always implicit (click behavior, reading time, etc). Inevitably, there are noises in implicit feedback. On one hand, the user may exit immediately after clicking the news as he dislikes the news content, leaving the noise in his positive implicit feedback; on the other hand, the user may be recommended multiple interesting news at the same time and only click one of them, producing the noise in his negative implicit feedback. Opposite implicit feedback could construct more integrated user preferences and help each other to minimize the noise influence. Previous works on news recommendation only used positive implicit feedback and suffered from the noise impact. In this paper, we propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN. DRPN utilizes both feedback for recommendation with a module to denoise both positive and negative implicit feedback to further enhance the performance. Experiments on the real-world large-scale dataset demonstrate the state-of-the-art performance of DRPN.

## introduction online news platforms, such as google news and microsoft news, have attracted a large population of users @xcite . however, massive news articles emerging every day on these platforms make it difficult for users to find appealing content quickly @xcite . to alleviate the information overload problem, recommender systems have become integral parts of these platforms. a core problem in news recommendation is how to learn better representations of users and news @xcite . early works include collaborate filtering (cf) based methods @xcite , content-based methods @xcite and hybrid methods @xcite that combine the two. these methods usually have the cold start problem when being exposed to the sparsity of user-item interactions @xcite . recently, deep learning methods have been proposed to learn better user and news representations. the techniques evolve from using recursive neural network @xcite , attention mechanism @xcite , to graph neural network @xcite @xcite . these methods usually recommend news for users based on their historical feedback. implicit feedback is more commonly collected than explicit feedback for news because the users usually do not grade the news. hence, current news recommendation methods naturally use positive implicit feedback like click behavior as the historical feedback to model user interests. however, there are gaps between positive implicit feedback and user real preferences @xcite . for example, the click behaviors do not fully reflect the user's preferences. the user may exit the news immediately after clicking, which will involve a noise in the positive feedback. additionally, some news that users did not click, may also attract them later. ignoring them also impacts the recommendation performance. our observation is that using both positive and negative implicit feedback can better model user interests. besides, positive and negative implicit feedback can help to denoise each other by conducting inter-comparison and intra-comparison. if a news story in one feedback sequence is more similar to the news in the opposite feedback sequence rather than the news in the same sequence, it is very likely that this news story constitutes noise. we can remove this news when building user interests. this idea is shown in figure 1 . in this paper, we propose the denoising neural network for news recommendation with positive and negative implicit feedback, named drpn. it first introduces a news encoder to represent the news in two implicit feedback sequences. then two parallel aggregators are used to extract user representations from both positive and negative historical feedback: (1) content-based aggregator, which selects the informative news in the feedback sequences to represent the user; (2) denoising aggregator, which finds and reduces the noises in the feedback sequences. in addition to the semantic information, we introduce a graph neural network to incorporate the collaborative information to further enrich the user representation. finally, the user and candidate news representations are used to predict the clicking probability. the contributions of this paper are summarized as follows: ## problem formulation the news recommendation problem in our paper can be illustrated as follows. let u and r denote the entire user set and news set. the feedback matrix for the users over the news is denoted as @xmath0, where z u,@xmath1.g., u clicks r), z u,@xmath2.g., u sees r but ignores it), and z u,@xmath3. l u and l r denote the numbers of the users and news, respectively. for each specific user, his historical positive feedback sequence [p 1 , ..., p lp ] and negative feedback sequence [n 1 , ..., n ln ] can be gathered from the feedback matrix z, where p i , n @xmath4. given the feedback matrix z, the goal is to train a model m (i.e., drpn). for each new pair of user and candidate news (@xmath5, @xmath6, we can use m to estimate the probability that u would like to click r. da ca 𝑟 𝑐 𝑡 title encoder … 𝑝 1 𝑜 𝑝 𝑙 𝑝 𝑜 𝑛 𝑙 𝑛 𝑜 … 𝑟 𝑐 𝑜 news id embedding 𝒑 𝟏 𝒐 𝒑 𝒍 𝒑 𝒐 𝒏 𝒍 𝒏 𝒐 𝒓 𝒄 𝒐 … collaborative interest encoder 𝒑 𝒔 𝒕 𝒏 𝒔 𝒕 𝒑 𝒉 𝒕 𝒏 𝒉 𝒕 𝒑 𝒔 𝒐 𝒏 𝒔 𝒐 𝒑 𝒉 𝒐 𝒏 𝒉 𝒐 fusion net fusion net 𝒖 𝒕 𝒖 𝒐 gnn ො 𝑦 𝑛 𝑙 𝑛 𝑡 𝑛 1 𝑡 … 𝑝 𝑙 𝑝 𝑡 𝑝 1 𝑡 𝒓 𝒄 𝒕 𝒑 𝟏 𝒕 𝒑 𝒍 𝒑 𝒕 … 𝒏 𝟏 𝒕 𝒏 𝒍 𝒏 𝒕 ca da semantic interest encoder … 𝑛 1 𝑜 … 𝒏 𝟏 𝒐 … figure 2: the drpn framework. ## framework figure 2 shows the architecture of drpn. it first employs the title encoder and id embedding layer to represent all news in two feedback sequences and the candidate news. then two separate encoders are employed to extract the user semantic interest and collaborative interest information from both positive and negative implicit feedback sequences. next, two fusion nets combine multiple interest representations to represent the user. finally, we use the user and candidate news representations to estimate the clicking probability. we will detail each component in the following subsections. ## experiment 5.1 dataset there is no off-the-shelf dataset in which the user profile includes both positive and negative historical feedback sequences. therefore, we use mind foot_0 dataset (its original user profile only contains positive feedback) to re-build one to conduct the experiments. the original mind dataset contains the user impression logs. an impression log records the news displayed to a user when visiting the news website homepage at a specific time, and the click behaviors on the news list. we re-build the dataset based on the mind's impression logs as follows: then we add the news that a user has seen but did not click to his negative feedback sequence, and add the news he clicked to his positive feedback sequence. in this manner, the user profile includes both positive and negative historical feedback sequences; (2) training set: the impression logs of 6-th day of the original training set; (3) validation set: the first 10% chronological impression logs of the original validation set; (4) testing set: the last 90% chronological impression logs of the original validation set. the training, validation, and testing sets use the same user profile built in step (1). since the user profiles are only built in step (1) which is ahead of step ( 2 )-( 4 ), there is no label leakage to validation and testing sets. moreover, same as the original mind dataset, the re-built dataset also has 44.6% users of validation set and 48.7% users of test set that are not shown in the re-built training set. ## conclusion in this paper, we propose a novel deep neural news recommendation model drpn. in drpn, we design two aggregators to extract user interests from both positive and negative implicit feedback. the content-based aggregator focuses on the contents in the news representations and the denoising aggregator aims to mitigate the noise impact commonly existing in the implicit feedback. besides, apart from the title information, drpn also exploits the collaborative information by the graph neural network to further improve the recommendation performance. experimental results on a large-scale public dataset demonstrate the state-of-the-art performance of drpn. the further study results also show the effectiveness of the denoising module.