2023
M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework
Zerong Lan, Yingyi Zhang, Xianneng Li# (# corresponding author)
In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23) 2023
Users in recommender systems exhibit multi-behavior in multiple business scenarios on real-world e-commerce platforms. A crucial challenge in such systems is to make recommendations for each business scenario at the same time. On top of this, multiple predictions (e.g., Click Through Rate and Conversion Rate) need to be made simultaneously in order to improve the platform revenue. Research focus on making recommendations for several business scenarios is in the field of Multi-Scenario Recommendation (MSR), and Multi-Task Recommendation (MTR) mainly attempts to solve the possible problems in collaboratively executing different recommendation tasks. However, existing researchers have paid attention to either MSR or MTR, ignoring the integration of MSR and MTR that faces the issue of conflict between scenarios and tasks. To address the above issue, we propose a Meta-based Multi-scenario Multi-task RECommendation framework (M3REC) to serve multiple tasks in multiple business scenarios by a unified model. However, integrating MSR and MTR in a proper manner is non-trivial due to: 1) Unified representation problem: Users’ and items’ representation behave Non-i.i.d in different scenarios and tasks which takes inconsistency into recommendations. 2) Synchronous optimization problem: Tasks distribution varies in different scenarios, and a unified optimization method is needed to optimize multi-tasks in multi-scenarios. Thus, to unified represent users and items, we design a Meta-Item-Embedding Generator (MIEG) and a User-Preference Transformer (UPT). The MIEG module can generate initialized item embedding using item features through meta-learning technology, and the UPT module can transfer user preferences in other scenarios. Besides, the M3REC framework uses a specifically designed backbone network together with a task-specific aggregate gate to promote all tasks to achieve the purpose of optimizing multiple tasks in multiple business scenarios within one model. Experiments on two public datasets have shown that M3REC outperforms those compared MSR and MTR state-of-the-art methods.
Meta-Generator Enhanced Multi-Domain Recommendation
Yingyi Zhang, Xianneng Li#, Yahe Yu, ect. (# corresponding author)
In Companion Proceedings of the ACM Web Conference 2023 (WWW’23 Companion - industry truck) 2023
Large-scale e-commercial platforms usually contain multiple business fields, which require industrial algorithms to characterize user intents across multiple domains. Numerous efforts have been made in user multi-domain intent modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the domains having rich user information, which makes implementation to domains with sparse or rare user behavior meet with mixed success. Hence, in this paper, we propose a novel method named Meta-generator enhanced multi-Domain model (MetaDomain) to address the above issue. MetaDomain mainly includes two steps, 1) users’ multi-domain intent representation and 2) users’ multi-domain intent fusion. Specifically, in users’ multi-domain intent representation, we use the gradient information from a domain intent extractor to train the domain intent meta-generator, where the domain intent extractor has the input of users’ sequence feature and domain meta-generator has the input of users’ basic feature, hence the capability of generating users’ intent with sparse behavior. Afterward, in users’ multi-domain intent fusion, a domain graph is used to represent the high-order multi-domain connectivity. Extensive experiments have been carried out under a real-world industrial platform named Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed MetaDomain method over the state-of-the-art baselines. Furthermore comparing with the method using multi-domain sequence features, MetaDomain can reduce the serving latency by 20%. Currently, MetaDomain has been deployed in Meituan one of the largest worldwide Online-to-Offline(O2O) platforms.
Deep review-based recommendation from the perspective of consumer decision journey
Yingyi Zhang, Xianneng Li, Yanhong Guo, Xiaogang Li#, Shuang Zheng (# corresponding author)
Journal of Management Sciences in China 2023
The essence of recommender systems is to model the implicit preferences in consumer behavior. The human behavior is inseparable from psychology, and there are rich internal motives behind the superficial behavior. However, the current studies mainly focus on the behavioral data modeling, rarely involve the internal psychological activities and the information processing process in decision-making. Therefore, this paper studied a new idea of recommender systems by introducing AIDMA decision model from the perspective of consumer decision journey. This paper proposed a new deep review-based recommender system, which applies the AIDMA decision journey into the deep learning framework. Experiments showed that the recommendation performance of the proposal is significantly better than the state-of-the-art methods. This paper follows the big data-driven research paradigm of "model driven + data-driven", realizing the in-depth method innovation with theoretical support.
2022
Entire Cost Enhanced Multi-Task Model for Online-to-Offline Conversion Rate Prediction
Yingyi Zhang, Xianneng Li#, Yahe Yu, ect. (# corresponding author)
DL4SR’22: Workshop on Deep Learning for Search and Recommendation, co-located with the 31st ACM International Conference on Information and Knowledge Management (CIKM) 2022
Predicting users’ conversion rate (CVR) is essentially important for ranking systems in industrial Online-to-Offline (O2O) applications. Numerous efforts have been made in CVR modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the Business-to-Customer (B2C) scenario, which makes implementations to O2O meet with mixed success. This can be revealed via several scenario-specific challenges. For example, O2O users in different locations generally encounter different candidates of surrounding stores. This leads to users’ behavioral regularity becoming essentially prominent. Besides, O2O users’ conversion includes a two-stage cost, i.e., online order cost and offline transportation cost. This inspires that users’ location sensitivity deserves additional attention compared with conventional scenarios. Motivated by these characteristics, we propose a novel CVR prediction method for the O2O scenario, named Entire Cost enhanced Multi-task Model (ECMM): i) users’ historical behavior sequences across different locations are modeled to capture the users’ preference of behavioral regularity; ii) both online order cost and offline transportation cost are modeled to predict the users’ aggregated preference for conversion. By designing two novel attention mechanisms, i.e., convert attention and sliding window attention, ECMM can be trained end-to-end to appropriately fit O2O characteristics. Extensive experiments have been carried out under a real-world industrial O2O platform Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed ECMM over the highly optimized state-of-the-art baselines.