Inferring user interests in microblogging social networks: a survey

Piao, Guangyuan; Breslin, John G.

doi:10.1007/s11257-018-9207-8

Cited by 41 publications

(23 citation statements)

References 98 publications

(255 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other user characteristics include cognitive and physical ability (Loitsch et al. 2017), knowledge (Pelánek 2017), interests (Piao and Breslin 2018), and affect (Mizgajski and Morzy 2018; Grawemeyer et al. 2017).…”

mentioning

confidence: 99%

A methodology for creating and validating psychological stories for conveying and measuring psychological traits

Smith

Dennis

Masthoff

et al. 2019

User Model User-Adap Inter

View full text Add to dashboard Cite

Personality impacts all areas of our lives; it governs who we are and how we react to life’s challenges. Personalized systems that adapt to end users should take into account the user’s personality to perform well. Several methodologies (e.g. User-as-Wizard, indirect studies) that use personality adaptation require first for personality to be conveyed to the participant; this has few validated approaches. Furthermore, measuring personality is often time consuming, prone to response bias (e.g. using questionnaires) or data intensive (e.g. using behaviour or text mining). This paper presents a methodology for creating and validating stories to convey psychological traits and for using such stories with a personality slider scale to measure these traits. We present the validation of the scale and evaluate its reliability. To evidence the validity of the methodology, we outline studies where the stories and scale have been effectively applied (in recommender systems, intelligent tutoring systems, and persuasive systems).

show abstract

mentioning

confidence: 99%

A methodology for creating and validating psychological stories for conveying and measuring psychological traits

Smith

Dennis

Masthoff

et al. 2019

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…It also separates agent Chuck from the other agents who show a preference towards maintaining a high-speed and the left lane, because her feature expectations are "far" from the others, even though the objectives are the same. Inferring the users' interests and intentions is a relevant problem in this context with a variety of possible applications (Piao and Breslin 2018). For instance, understanding why users perform certain actions, like posting a message or clicking on an ad, and what their preferences are, allows the system to provide personalized recommendations and, in general, improve the user experience.…”

Section: Resultsmentioning

confidence: 99%

“…Several learning-based techniques have been designed for this problem (Saravia et al 2017;Song et al 2015;Xu et al 2011;Sadri et al 2019). We refer the reader to an interesting survey by Piao and Breslin (2018). To the best of our knowledge, the only previous work that has applied IRL to this problem is Das and Lavoie (2014), in which the authors presented an IRL-based algorithm to infer the intentions of Reddit users.…”

Section: Resultsmentioning

confidence: 99%

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

et al. 2021

View full text Add to dashboard Cite

In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

show abstract

“…In practice, developers could access content through the API without Twitter being able to examine ex ante the purpose and use of the data being collected, making the enforcement of these terms relatively ineffective. In the face of the spread of methods for user profiling (Piao & Breslin, 2017), individuals can develop tactics to prevent the discovery of their implicit attributes on social media (Nechaev, Corcoglioniti, & Giuliano, 2017); however, the procedure developed here would be immune to these countermeasures as it does not rely on the features of an individual's profile but on characterizations made by third parties which the individual does not control.…”

Section: Using Twitter (And Other Social Media) As a Data Setmentioning

confidence: 99%

Opening Fields: A Methodological Contribution to the Identification of Heterogeneous Actors in Unbounded Relational Orders

Benabdelkrim

Levallois

Savinien

et al. 2020

mgmt

View full text Add to dashboard Cite

Institutional scholarship studies how individuals coexist and interact with social structures. Organizations and inter-organizational relations within industries are a central focus of these studies. Hence, empirical research has so far largely relied on the observation of individual actors identified by their organizational attributes, and organizations identified by their industry characteristics. The flourishing of new types of social structures has sent an invitation to observe a broader range of actors beyond organizations stricto sensu, and to define the arena of interest beyond the boundaries of industry membership. However, in practice, these remain a favorite starting point of empirical investigations. In this article, we present a new method for the study of organizational fields that facilitates the identification of a large number and varied types of actors in a given field, provides a characterization of the relational structure of the field, and offers a content analysis on different sub-regions of the field. We test the method by replicating a previous study in the field of ‘social impact of nonprofits’, and show how it can contribute to operationalize mechanisms at play in the field. We conclude by noting that the principles of this method can extend beyond the dataset it is originally built on and facilitate a comparative approach to the study of fields. This contribution should enhance the value of the field as a theoretical construct by extending its operational reach.

show abstract

Inferring user interests in microblogging social networks: a survey

Cited by 41 publications

References 98 publications

A methodology for creating and validating psychological stories for conveying and measuring psychological traits

A methodology for creating and validating psychological stories for conveying and measuring psychological traits

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

Opening Fields: A Methodological Contribution to the Identification of Heterogeneous Actors in Unbounded Relational Orders

Contact Info

Product

Resources

About