Citation recommendation: approaches and datasets

Färber, Michael; Jatowt, Adam

doi:10.1007/s00799-020-00288-2

Cited by 68 publications

(44 citation statements)

References 149 publications

(278 reference statements)

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“…The proposed model has the potential to be applied to other domains as well. The lexicon can be generated with a fine-tuned lexicon to any other domains such as tourism [ 50 ], music [ 51 ] and citation recommendation [ 52 ], to produce an accurate and improvised recommender system. Future work includes the use of similar data sets of other previous works, so that a fair comparison can be made.…”

Section: Future Workmentioning

confidence: 99%

Integrating contextual sentiment analysis in collaborative recommender systems

et al. 2021

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Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers’ feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

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Section: Future Workmentioning

confidence: 99%

Integrating contextual sentiment analysis in collaborative recommender systems

et al. 2021

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“…For all models, we use Anserini's default parameters. We evaluate retrieval effectiveness in terms of mean average precision (mAP) on the top 1,000 retrieved documents for ad-hoc document retrieval, and in terms of recall at 10 retrieved documents for context-aware citation recommendation as recommended in (Färber and Jatowt, 2020). We use the Student's paired t-test to assess statistical significance of our retrieval results at p < 0.05 (Smucker et al, 2007…”

Section: Experimental Settingsmentioning

confidence: 99%

Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness

Boudin¹,

Gallina²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.

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“…Compared to global CR, local CR has attracted less attention, presumably as there is fewer datasets with extracted citation contexts. A comprehensive overview of local CR is (Färber and Jatowt, 2020).…”

Section: Related Workmentioning

confidence: 99%

Improved Local Citation Recommendation Based on Context Enhanced with Global Information

Medić¹,

Šnajder²

2020

Proceedings of the First Workshop on Scholarly Document Processing

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Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article's title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globallyenhanced context representations when citation contexts are smaller.1 Most work on local CR uses contexts from published articles with citations masked out. While this is a somewhat artificial setup as those articles are biased toward existing citations, local CR can nonetheless be used in this setup to recommend additional citations.

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Citation recommendation: approaches and datasets

Cited by 68 publications

References 149 publications

Integrating contextual sentiment analysis in collaborative recommender systems

Integrating contextual sentiment analysis in collaborative recommender systems

Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness

Improved Local Citation Recommendation Based on Context Enhanced with Global Information

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