Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

Piao, Guangyuan; Breslin, John G.

doi:10.1145/2993318.2993332

Cited by 40 publications

(28 citation statements)

References 24 publications

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“…The potential of detecting other user signals such as learner interest from PEEK dataset can also lead to insightful findings. PEEK dataset also allows experimenting with temporal dynamics such as interest decay [45].…”

Section: Discussionmentioning

confidence: 99%

PEEK: A Large Dataset of Learner Engagement with Educational Videos

Bulathwela,

Perez-Ortiz,

Novak

et al. 2021

Preprint

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In this work, we release a large and novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics (PEEK), is one of the first publicly available datasets that address personalised educational engagement. Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient personalised learning systems could improve learning gains significantly. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In the PEEK dataset, educational video lectures have been associated with Wikipedia concepts related to the material of the lecture, thus providing a humanly intuitive taxonomy. We believe that granular learner engagement signals, in unison with rich content representations, will pave the way to building powerful personalisation algorithms that will revolutionise educational and informational recommendation systems. Towards this goal, we 1) construct a novel dataset from a popular video lecture repository, 2) identify a set of benchmark algorithms to model engagement, and 3) run extensive experimentation on the PEEK dataset to demonstrate its value. Our experiments with the dataset show promise in building powerful informational recommender systems. The dataset and the support code is available at https://github.com/sahanbull/PEEK-Dataset.

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Section: Discussionmentioning

confidence: 99%

PEEK: A Large Dataset of Learner Engagement with Educational Videos

Bulathwela,

Perez-Ortiz,

Novak

et al. 2021

Preprint

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“…Such slowly varying matrices arise in a lot of applications like manufacturing systems, internet packet transmission and wireless communication (see Section 1.3 in [28]). Also, the user interest in online social media typically evolves slowly [29]. 9 The transition matrices computed on real dataset in Section 5 follow a tridiagonal structure; refer to (24).…”

Section: Discussion Of Assumptionsmentioning

confidence: 99%

Multiple stopping time POMDPs: Structural results & application in interactive advertising on social media

2018

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This paper considers a multiple stopping time problem for a Markov chain observed in noise, where a decision maker chooses at most L stopping times to maximize a cumulative objective. We formulate the problem as a Partially Observed Markov Decision Process (POMDP) and derive structural results for the optimal multiple stopping policy. The main results are as follows: i) The optimal multiple stopping policy is shown to be characterized by threshold curves Γ l , for l = 1, · · · , L, in the unit simplex of Bayesian Posteriors. ii) The stopping sets S l (defined by the threshold curves Γ l ) are shown to exhibit the following nested structure S l−1 ⊂ S l . iii) The optimal cumulative reward is shown to be monotone with respect to the copositive ordering of the transition matrix. iv) A stochastic gradient algorithm is provided for estimating linear threshold policies by exploiting the structural results. These linear threshold policies approximate the threshold curves Γ l , and share the monotone structure of the optimal multiple stopping policy. As an illustrative example, we apply the multiple stopping framework to interactively schedule advertisements in live online social media. It is shown that advertisement scheduling using multiple stopping performs significantly better than currently used methods.

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“…ey also focused on temporal pattern extraction in users' profile. Piao and Breslin [37] analyzed user modeling strategies by incorporating categories, classes, and connected entities from DBpedia for extending user interest profiles and found that their proposed method significantly outperforms existing approaches in the context of link recommendations. A dynamic user modeling-based recommendation system was proposed by Deng et al [38] to integrate information extracted from tweets and the video ranking system employed by Youtube based on the same user's profile.…”

Section: Related Workmentioning

confidence: 99%

Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

et al. 2020

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Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.

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Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

Cited by 40 publications

References 24 publications

PEEK: A Large Dataset of Learner Engagement with Educational Videos

PEEK: A Large Dataset of Learner Engagement with Educational Videos

Multiple stopping time POMDPs: Structural results & application in interactive advertising on social media

Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

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