Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video ad platforms, users tend to quickly slip away from ad candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedback and make adjustments to avoid these recommendations.Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance.In conclusion, we propose Pareto-based Multi-Objective Recommender System with forgetting curve (PMORS), which can be applied to any multi-objective recommendation and show sufficiently superiority when facing explicit negative feedback.We have conducted evaluations of PMORS and achieved favorable outcomes in short-video scenarios on both public dataset and industrial dataset. After being deployed on an online short video ad platform named WeChat Channels Ads in May, 2023, PMORS has not only demonstrated promising results for both consistency and recency but also achieved an improvement of up to +1.45% Gross Merchandise Volume (GMV).

Estimating conversion rate (CVR) accurately has been one of the most central problems in online advertising. Existing methods in production focus on learning effective interactions among features to boost the model performance. Despite great success, these methods treat all the features equally without distinction. However, different features suffer differently from cold-start issues. Tail elements in those high-cardinality features, which we denote as fine-grained features, tend to have inadequate samples and thus fail to obtain semantically meaningful embeddings. Interacting with those features leads astray and impairs the accuracy of new ads in a cold-start scenario. In this paper, we propose Automatic Fusion Network (AutoFuse) to better tackle the challenge. AutoFuse explicitly separates features into groups based on their granularity and learns multiple levels of representation conditioned on different combinations of feature groups. Concretely, AutoFuse learns an ad-level representation to depict the unique individual character and a group-level representation to portray the collective information by discarding the fine-grained features. The final robust and general ad representation is obtained by integrating these two level representations adaptively. Such a combination encompasses a wider amount of information, and thereby mitigates the cold-start issue. Extensive experiments on two industrial-scale datasets and three public datasets show that AutoFuse significantly and consistently outperforms a spectrum of competitive methods including our currently deployed model. Meanwhile, the remarkable improvement on new ads validates the effectiveness of our method in cold-start scenarios. We design AutoFuse as a generic approach and thus it can be seamlessly transferred into other domains. Our method has been deployed online to serve billions of users and ads and has achieved significant GMV gain of 2.84%.

Existing online advertising systems employ separate models for each task and site, resulting in a large number of models that require significant computing power and human effort to train and deploy. Moreover, separate models have limitations in sharing cross-scenario information. To address these issues, we propose a unified sequential recommendation model called SCRIPT. It takes cross-scenario user behavior sequences as input and explicitly incorporates meta information that characterizes scenario features, such as domain, site, and behavior types. Inspired by the advances of the pretrain and prompt paradigm, we generate scenario-aware and personalized prompts based on the user profile and meta information of candidate items. This allows the model to leverage the knowledge learned during pre-training and adapt it to serve different downstream tasks. Extensive experiments on two public dataset and a production dataset demonstrate that our model achieves state-of-the-art performance on multiple downstream recommendation tasks.

Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackles a semi-supervised learning problem where most interaction data are unobserved. Such a nature makes existing approaches highly rely on mining negatives for providing correct training signals. However, mining proper negatives is not a free lunch, encountering with a tricky trade-off between mining informative hard negatives and avoiding false ones. We devise a new approach named as Hardness-Aware Debiased Contrastive Collaborative Filtering (HDCCF) to resolve the dilemma. It could sufficiently explore hard negatives from two-fold aspects: 1) adaptively sharpening the gradients of harder instances through a set-wise objective, and 2) implicitly leveraging item/user frequency information with a new sampling strategy. To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution. Extensive experiments demonstrate the superiority of the proposed model over existing CF models and hard negative mining methods.