Topic-based hierarchical Bayesian linear regression models for niche items recommendation

There has been a proliferation of new research discovery tools that aid scientists in finding relevant publications. To obtain a general overview of this development, this article generates a conceptual typology of all possible research discovery tools by drawing from the information-theoretical concepts of redundancy/variety. Bibliometric links between scholarly publications can thus exhibit ‘redundancy’ (i.e. expectable linkages between academic works) or ‘variety’ (i.e. original co-occurrence patterns). On the redundancy-reproducing end of the typology are machines that harness extant co-citations or keyword queries, such as academic search engines and paper recommender systems. The variety end of the spectrum harbours services that enable categorial browsing or that suggest publications randomly, such as journals’ tables of contents or random paper bots. The typology has implications for understanding how the design of research discovery platforms may ultimately shape aggregate citational networks of science.

Section: Redundancy-generating Toolsmentioning

confidence: 99%

A typology of research discovery tools

Nishikawa-Pacher

2021

“…Hence, the core of the problem is to define the differences between attractions according to their features, in which the important part is to comprehensively understand and express the features of attractions. In recent years, topic analysis has been actively studied in the feature extraction and has gradually become an important feature analysis technology [42][43][44]. A number of researches in the field of tourism have described the features of attractions through a set of related topics [17,45].…”

Section: Optimisation Model Considering Topic Diversitymentioning

confidence: 99%

Personalised attraction recommendation for enhancing topic diversity and accuracy

Lin

Huang

Yao

et al. 2021

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

“…Existing topic models are mainly applied in the field of business intelligence [5,9 –12] and social media [4,13 –15], which may be inappropriate with online learning platforms. First, since the character length of each post or review in social platforms (e.g.…”

Section: Introductionmentioning

confidence: 99%

SBTM: A joint sentiment and behaviour topic model for online course discussion forums

Peng

Gan

2020

Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.