Towards a Multi-View Attentive Matching for Personalized Expert Finding

Peng, Qiyao; Liu, Hongtao; Wang, Yinghui; Xu, Hongyan; Jiao, Pengfei; Shao, Ming; Wang, Wenjun

doi:10.1145/3485447.3512086

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…StackExchange data has been used in several EF papers, e.g., in [8,13,17]. These works however mostly focus on solving the expert finding task for a single community.…”

Section: Se-pef Definitionmentioning

confidence: 99%

“…Furthermore, most works among those previously cited either rely on a private dataset, or refer to a specific domain and make very strong assumptions simplifying the task addressed. Conversely, SE-PEF will be made publicly available, it has a well-defined definition of an expert, which is inspired by reasonable hypothesis common to other works [11,13,17]. Furthermore, it provides a rich set of social features usable for personalization and combines data from multiple communities, which, as we have already stated, increases dataset diversity and opens the possibility of exploiting cross-domain user information for EF.…”

Section: Comparison With Available Datasetsmentioning

confidence: 99%

“…In [10] the authors show that personalization is more useful if multiple communities are used together in this dataset rather than using a single community to create the dataset. Meanwhile, previous works that use StackExchange for EF tasks focus only on a single community or a portion of a community, thus neglecting the domain diversity characterizing the questions and the various experts [8,13,17].…”

mentioning

confidence: 99%

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SE-PEF: a Resource for Personalized Expert Finding

Kasela,

Pasi,

Perego

2023

Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia P

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The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets to support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange -Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the Expert Finding (EF) task. The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.

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“…StackExchange data has been used in several EF papers, e.g., in [8,13,17]. These works however mostly focus on solving the expert finding task for a single community.…”

Section: Se-pef Definitionmentioning

confidence: 99%

Section: Comparison With Available Datasetsmentioning

confidence: 99%

mentioning

confidence: 99%

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SE-PEF: a Resource for Personalized Expert Finding

Kasela,

Pasi,

Perego

2023

Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia P

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“…Based on the powerful neural network (LeCun et al, 2015), many approaches (Li et al, 2019;Fu et al, 2020;Peng et al, 2022a) have been shown effective to improve the performance of expert finding. The core idea is to first model the question semantically, and then learn the expert preference based on his/her historically answered questions.…”

Section: Introductionmentioning

confidence: 99%

“…The core idea is to first model the question semantically, and then learn the expert preference based on his/her historically answered questions. For example, Peng et al (Peng et al, 2022a) proposed PMEF equipped with a multi-view question modeling paradigm aiming to model questions more comprehensively and then capture expert features. Hence, the capacity of the designed question modeling paradigm directly affects the expert finding performance.…”

Section: Introductionmentioning

confidence: 99%

Contrastive Pre-training for Personalized Expert Finding

Peng,

Liu,

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pretraining phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.

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