In computational advertising, platforms are increasingly optimizing toward advertisers' real assessment metrics to help achieve more reliable advertising performance. Consequently, predicting customers' Lifetime Value (LTV) has become an essential component of the advertising system, as it directly impacts the actual Return On Investment (ROI) of advertisers. Recent research on LTV prediction primarily focuses on the ranking stage, lacking consideration of the initial retrieval stage. This oversight may lead to the inconsistency between retrieval and ranking, resulting in a loss of efficiency. Unlike the LTV estimation in the ranking stage, the retrieval stage faces more severe data sparsity and constraints inherent in online scoring. Incorporating rich data from other domains can mitigate the sparsity while introducing the negative transfer issue. To tackle these challenges, we introduce Retrieval-LTV, a two-tower retrieval model for LTV prediction. This model employs a cooperative framework and incorporates a fine-grained evaluation for each sample across each expert, thereby enhancing effective selective learning from the source domain while mitigating the risk of negative transfer. Additionally, we have designed a specialized representation transformation to obtain the LTV-oriented score for online retrieval. Experiments on three real-world industrial datasets demonstrate that Retrieval-LTV outperforms all the baselines, achieving superior performance. An online A/B test further confirms the effectiveness of Retrieval-LTV, increasing the overall LTV by 2.08%. As a result, Retrieval-LTV has now been fully deployed in Tencent Ads.

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conflicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. The model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output confidence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. The experimental results showed that compared with other models, our model achieved significant and consistent improvements.

Named entity relation extraction is an important subject in the field of information extraction. Although many English extractors have achieved reasonable performance, an effective system for Chinese relation extraction remains undeveloped due to the lack of Chinese annotation corpora and the specificity of Chinese linguistics. Here, we summarize three kinds of unique but common phenomena in Chinese linguistics. In this article, we investigate unsupervised linguistics-based Chinese open relation extraction (ORE), which can automatically discover arbitrary relations without any manually labeled datasets, and research the establishment of a large-scale corpus. By mapping the entity relations into dependency-trees and considering the unique Chinese linguistic characteristics, we propose a novel unsupervised Chinese ORE model based on Dependency Semantic Normal Forms (DSNFs). This model imposes no restrictions on the relative positions among entities and relationships and achieves a high yield by extracting relations mediated by verbs or nouns and processing the parallel clauses. Empirical results from our model demonstrate the effectiveness of this method, which obtains stable performance on four heterogeneous datasets and achieves better precision and recall in comparison with several Chinese ORE systems. Furthermore, a large-scale knowledge base of entity and relation, called COER, is established and published by applying our method to web text, which conquers the trouble of lack of Chinese corpora.