Author name cluster

Linjian Mo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2025 Conference Paper

DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval

Penghao Lu
Xin Dong
Yuansheng Zhou
Lei Cheng
Chuan Yuan
Linjian Mo

Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models(LM) to generate a ranked list of document identifiers (docid) for a given query. It simplifies the retrieval pipeline by replacing the large external index with model parameters. However, existing works merely learned the relationship between queries and document identifiers, which is unable to directly represent the relevance between queries and documents. To address the above problem, we propose a novel and general generative retrieval framework, namely Leveraging Document-Oriented Contrastive Learning in Generative Retrieval (DOGR), which leverages contrastive learning to improve generative retrieval tasks. It adopts a two-stage learning strategy that captures the relationship between queries and documents comprehensively through direct interactions. Furthermore, negative sampling methods and corresponding contrastive learning objectives are implemented to enhance the learning of semantic representations, thereby promoting a thorough comprehension of the relationship between queries and documents. Experimental results demonstrate that DOGR achieves state-of-the-art performance compared to existing generative retrieval methods on two public benchmark datasets. Further experiments have shown that our framework is generally effective for common identifier construction techniques.

PDF Details DOI

AAMAS Conference 2023 Conference Paper

Model-Based Reinforcement Learning for Auto-bidding in Display Advertising

Shuang Chen
Qisen Xu
Liang Zhang
Yongbo Jin
Wenhao Li
Linjian Mo

Real-time bidding (RTB) achieves outstanding success in online display advertising, which has become one of the most influential businesses. Given historical ad impressions under the second price auction mechanism, the advertiser’s optimal bidding strategy is determined by the core parameter corresponding to the optimal solution of a constrained optimization problem. However, the sequentially arrived impressions in online display advertising make it highly non-trivial to obtain the optimal core parameter in advance without knowing the complete impression set. For this reason, recent methods have generally transformed the core parameter determination problem into a sequential parameter adjustment problem and solved it using reinforcement learning (RL). This paper proposes a simple and effective Model-Based Automatic Bidding algorithm, MBAB, which explicitly models the uncertainty of the dynamic auction environment and then uses the dynamic programming algorithm to obtain the current optimal adjustment of the core parameter. MBAB can avoid burdensome simulated environment construction and is more suitable for production deployment without the thorny sim-to-real issue than model-free methods. Furthermore, MBAB uses the optimal bidding formula to carry out coarse-grained modeling of the online market environment to alleviate the scalability problem caused by fine-grained environment modeling of previous model-based methods. In order to accurately describe the impression distribution and non-stationarity of the online market environment, we introduce the probabilistic modeling method and propose a novel monotonicity constraint to regulate the model output. Numerical experiments show that the proposed MBAB substantially outperforms existing baselines on various constrained RTB tasks in the production environment.

PDF

AAAI Conference 2023 Conference Paper

REMIT: Reinforced Multi-Interest Transfer for Cross-Domain Recommendation

Caiqi Sun
Jiewei Gu
Binbin Hu
Xin Dong
Hai Li
Lei Cheng
Linjian Mo

Cold-start problem is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary source domain to improve the performance of recommender system in the target domain. In particular, the family of embedding and mapping methods for CDR is very effective, which explicitly learn a mapping function from source embeddings to target embeddings to transfer user’s preferences. Recent works usually transfer an overall source embedding by modeling a common or personalized preference bridge for all users. However, a unified user embedding cannot reflect the user’s multiple interests in auxiliary source domain. In this paper, we propose a novel framework called reinforced multi-interest transfer for CDR (REMIT). Specifically, we first construct a heterogeneous information network and employ different meta-path based aggregations to get user’s multiple interests in source domain, then transform different interest embeddings with different meta-generated personalized bridge functions for each user. To better coordinate the transformed user interest embeddings and the item embedding in target domain, we systematically develop a reinforced method to dynamically assign weights to transformed interests for different training instances and optimize the performance of target model. In addition, the REMIT is a general framework that can be applied upon various base models in target domain. Our extensive experimental results on large real-world datasets demonstrate the superior performance and compatibility of REMIT.

PDF Details DOI