An overview and evaluation of citation recommendation models

Ali, Zafar; Ullah, Irfan; Khan, Amin Ullah; Jan, Asim Ullah; Muhammad, Khan

doi:10.1007/s11192-021-03909-y

Cited by 27 publications

(6 citation statements)

References 126 publications

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“…In existing studies [2,107,138], researchers suggest using multiple diverse datasets (e.g., more domains [138] and more sparsity levels [2]) for evaluating recommender systems. Note that the four Amazon datasets are selected based on the correlation results from [138], where they ind that there are four coherent groups (we select one representative dataset from each group).…”

Section: Observations and Discussionmentioning

confidence: 99%

A Revisiting Study of Appropriate Offline Evaluation for Top- N Recommendation Algorithms

Zhao

Lin

Feng

et al. 2022

ACM Trans. Inf. Syst.

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In recommender system, top- N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top- N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It becomes emergent to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select twelve top- N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open-sourced recommendation library, Recbole [139], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison.

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Section: Observations and Discussionmentioning

confidence: 99%

A Revisiting Study of Appropriate Offline Evaluation for Top- N Recommendation Algorithms

Zhao

Lin

Feng

et al. 2022

ACM Trans. Inf. Syst.

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“…Scientific facts should be presented accurately, methods to examine the correctness of the generated results should therefore be included. Moreover, there are increasing research results regarding to citation recommendation (Nogueira et al, 2020;Giosa and Di Caro, 2020;Ali et al, 2020;Khadka, 2020;Ali et al, 2021). Pipelines combining citation recommendation and generation can make the workflow of reviewing scientific literature more efficient.…”

Section: Future Workmentioning

confidence: 99%

Towards Generating Citation Sentences for Multiple References with Intent Control

Shieh

Hsu

et al. 2021

Preprint

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Machine-generated citation sentences can aid automated scientific literature review and assist article writing. Current methods in generating citation text were limited to single citation generation using the citing document and a cited document as input. However, in realworld situations, writers often summarize several studies in one sentence or discuss relevant information across the entire paragraph. In addition, multiple citation intents have been previously identified, implying that writers may need control over the intents of generated sentences to cover different scenarios. Therefore, this work focuses on generating multiple citations and releasing a newly collected dataset named CiteMI to drive the future research. We first build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs. Second, we incorporate the predicted citation intents into training for intent control. The experiments demonstrate that the proposed approaches provide much more comprehensive features for generating citation sentences.

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“…Research paper recommendation is a well-known and popular topic [1,42]. Specifically for citation recommendation [43], one distinguishes between recommendations based on a partial set of references and recommendations based on the content of a manuscript [44].…”

Section: Research Paper and Citation Recommendationmentioning

confidence: 99%

Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities

Vagliano¹,

Galke²,

Scherp³

2021

Preprint

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We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender performance. We also investigate data sparsity through a pruning parameter, and the influence of using additional metadata. As recommender models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. This outcome means that the semantics of item co-occurrence is an important factor. The simple item co-occurrence model is a strong baseline for citation recommendation. However, autoencoders have the advantage to enable exploiting additional metadata besides the partial item set as input, and achieve comparable performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the result. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit.

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An overview and evaluation of citation recommendation models

Cited by 27 publications

References 126 publications

A Revisiting Study of Appropriate Offline Evaluation for Top- N Recommendation Algorithms

A Revisiting Study of Appropriate Offline Evaluation for Top- N Recommendation Algorithms

Towards Generating Citation Sentences for Multiple References with Intent Control

Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities

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