Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia

Tommaso, Giorgia Di; Faralli, Stefano; Stilo, Giovanni; Velardi, Paola

doi:10.1007/978-3-030-00668-6_3

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…In order to address the semantic issues, research works have incorporated knowledge bases such as Wikipedia [29,[55][56][57][58][59][60][61], DBPedia [57,[62][63][64], WordNet [65][66][67], Freebase [68], Linked Open Data (LOD) cloud [49,69,70], and YAGO [71] to semantically represent user models. Semantic enrichment in the user modeling process is motivated by the need to enhance the accuracy of user models [22], increase the breadth of the keyphrases used to represent the users' interests [22,49], gather additional contextual knowledge about the entities and the relationships between them [30,49], infer more transparent and serendipitous user models [56], and bypass the problems of acronyms, synonyms, lexical variants [29], and polysemy, i.e., when a word may have multiple meanings which cannot be distinguished using keyword-based representation [30].…”

Section: Interest Model Generationmentioning

confidence: 99%

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Guesmi,

Chatti,

Kadhim

et al. 2023

MTI

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The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the realm of scientific publication recommendations. In a content-based scientific publication RS, recommendations are composed by observing the features of users and papers. Content-based recommendation encompasses three primary steps, namely, item representation, user modeling, and recommendation generation. A crucial part of generating recommendations is the user modeling process. Nevertheless, this step is often neglected in existing content-based scientific publication RS. Moreover, most existing approaches do not capture the semantics of user models and papers. To address these limitations, in this paper we present a transparent Recommendation and Interest Modeling Application (RIMA), a content-based scientific publication RS that implicitly derives user interest models from their authored papers. To address the semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with knowledge bases to generate semantically-enriched user interest models, and additionally leverages pretrained transformer sentence encoders to represent user models and papers and compute their similarities. The effectiveness of our approach was assessed through an offline evaluation by conducting extensive experiments on various datasets along with user study (N = 22), demonstrating that (a) combining SIFRank and SqueezeBERT as an embedding-based keyphrase extraction method with DBpedia as a knowledge base improved the quality of the user interest modeling step, and (b) using the msmarco-distilbert-base-tas-b sentence transformer model achieved better results in the recommendation generation step.

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Section: Interest Model Generationmentioning

confidence: 99%

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Guesmi,

Chatti,

Kadhim

et al. 2023

MTI

View full text Add to dashboard Cite

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“…Intrinsic evaluation helps to assess the quality of the constructed user interest profiles based on user studies [4,12,14] while extrinsic evaluations measure the quality of the user interest profiles by looking at its impact on the effectiveness of other applications such as news recommendation and retweet prediction [15,19,20]. Then, we describe the existing benchmark datasets and evaluation metrics [17]. Next, we introduce different applications that have been taking advantage of user interest modeling from social media platforms to improve their services.…”

Section: Tutorial Outlinementioning

confidence: 99%

Mining User Interests from Social Media

Zarrinkalam

Piao

Faralli

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

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Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work.

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“…In this regard, evaluating the performance of different user modeling strategies based on different datasets or settings can provide a clear understanding of when to use what types of user profiles, which is important for researchers in different domains as well as third-party application providers with different types of content to be personalized. A recent work by Tommaso et al (2018) provides a user interests dataset which is useful in this context. It includes half million Twitter users with an average of 90 multi-domain preferences per user on music, books, etc., where those preferences are extracted from multiple platforms based on the messages of those Twitter users who also use Spotify 40 , Goodreads 41 , etc.…”

Section: Future Directionsmentioning

confidence: 99%

Inferring user interests in microblogging social networks: a survey

Piao

Breslin

2018

User Model User-Adap Inter

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With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.

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Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia

Cited by 7 publications

References 27 publications

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Mining User Interests from Social Media

Inferring user interests in microblogging social networks: a survey

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