Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Seitlinger, Paul; Kowald, Dominik; Kopeinik, Simone; Hasani-Mavriqi, Ilire; Lex, Elisabeth; Ley, Tobias

doi:10.1145/2740908.2743057

Cited by 22 publications

(8 citation statements)

References 37 publications

(54 reference statements)

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“…Evaluation protocol. We split the datasets into train and test sets (Cremonesi et al, 2008) and make sure that our evaluation protocol preserves the temporal order of the listening events, which simulates a real-world scenario in which we predict (genres of) future listening events based on past ones (Kowald et al, 2017b;Seitlinger et al, 2015). This also means that a classic k-fold crossvalidation evaluation protocol with random splits is not useful.…”

Section: Methodsmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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In this paper, we address the problem of modeling and predicting the music genre preferences of users. We introduce a novel user modeling approach, BLL u , which takes into account the popularity of music genres as well as temporal drifts of user listening behavior. To model these two factors, BLL u adopts a psychological model that describes how humans access information in their memory. We evaluate our approach on a standard dataset of Last.fm listening histories, which contains fine-grained music genre information. To investigate performance for different types of users, we assign each user a mainstreaminess value that corresponds to the distance between the user's music genre preferences and the music genre preferences of the (Last.fm) mainstream. We adopt BLL u to model the listening habits and to predict the music genre preferences of three user groups: listeners of (i) niche, low-mainstream music, (ii) mainstream music, and (iii) medium-mainstream music that lies in-between. Our results show that BLL u provides the highest accuracy for predicting music genre preferences, compared to five baselines: (i) group-based modeling, (ii) user-based collaborative filtering, (iii) item-based collaborative filtering, (iv) frequency-based modeling, and (v) recencybased modeling. Besides, we achieve the most substantial accuracy improvements for the low-mainstream group. We believe that our findings provide valuable insights into the design of music recommender systems.

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Section: Methodsmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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“…Tags were also seen as a way to negotiate the meaning of particular experiences and established shared understanding in a group of learners (Dennerlein, Seitlinger, Lex, & Ley, 2016). Several services made use of the tags produced, eg, recommender services for resources (Seitlinger et al, 2015) or tag recommenders that were intended to drive the consistency of how objects were described (Seitlinger et al, 2018).…”

Section: Knowledge Structuresmentioning

confidence: 99%

Knowledge structures for integrating working and learning: A reflection on a decade of learning technology research for workplace learning

Ley¹

2019

Brit J Educational Tech

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Designing intelligent services for workplace learning presents a special challenge for researchers and developers of learning technology. One of the reasons is that considering learning as a situated and social practice is nowhere so important than in the case where learning is tightly integrated with workplace practices. The current paper analyses the experience of more than 10 years of research intending to offer intelligent services through capturing and leveraging knowledge structures in workplace learning. The reflection looks at results of several European research projects that have promoted this view. From this analysis, I arrive at a dichotomy of guidance versus emergence that describes how the technologies foregrounded one or the other, and what the effects of these design choices have been. I derive conclusions for dealing with this design trade‐off in terms of conceptual, technological and empirical research.

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“…Research papers Model of human categorization [17,23,35] Activation processes in human memory [18,21,24,37] Informal learning se ings [5][6][7] Resource recommendations Research papers A ention-interpretation dynamics [15,34] Tag and time information [27,28] Recommendation evaluation Research papers Real-world folksonomies [20] Technology enhanced learning se ings [16] Hashtag recommendations…”

Section: Tag Recommendationsmentioning

confidence: 99%

“…Mimicking Attention-Interpretation Dynamics. Seitlinger et al [34] introduced the rst version of a CF-based recommendation approach that takes into consideration non-linear user-artifact dynamics, modeled by means of SUSTAIN. SUSTAIN (Supervised and Unsupervised STrati ed Adaptive Incremental Network) is a exible network model of human category learning that is thoroughly discussed in [29].…”

Section: Resource Recommendationsmentioning

confidence: 99%

The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

Kowald

Kopeinik

Lex

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and re ect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and/or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers.us, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.

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Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Cited by 22 publications

References 37 publications

Modeling Popularity and Temporal Drift of Music Genre Preferences

Modeling Popularity and Temporal Drift of Music Genre Preferences

Knowledge structures for integrating working and learning: A reflection on a decade of learning technology research for workplace learning

The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

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