Recommender system for developing new preferences and goals

Liang, Yu

doi:10.1145/3298689.3347054

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Develop Develop new preferences. [38,55,57] Develop deep learning and meta-cognitive approaches to learning.…”

Section: Goal Description Referencesmentioning

confidence: 99%

“…[60] Present users their filter bubbles (blind spots) to encourage them to explore new items [55] Cover all users' tastes Help the user discover all of their preferences.…”

Section: Goal Description Referencesmentioning

confidence: 99%

“…As a possible mechanism to achieve the sub-goal Broaden horizons, Sullivan et al [27] proposed to "Increase diversity gradually" by making small steps outside of users' current circle of interests. The second sub-goal Recognize blind spots in the user model was extracted from [38,54,55,[58][59][60]. For instance, Tintarev et al [59] investigated how they can help users to better understand their consumption profiles through visualizations aiming at revealing to users regions of the recommendation space that are unknown to them, i.e., blind-spots.…”

Section: Explorementioning

confidence: 99%

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Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

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This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.

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“…Develop Develop new preferences. [38,55,57] Develop deep learning and meta-cognitive approaches to learning.…”

Section: Goal Description Referencesmentioning

confidence: 99%

“…[60] Present users their filter bubbles (blind spots) to encourage them to explore new items [55] Cover all users' tastes Help the user discover all of their preferences.…”

Section: Goal Description Referencesmentioning

confidence: 99%

Section: Explorementioning

confidence: 99%

See 1 more Smart Citation

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

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“…In the past, users mainly relied on recommendation systems to make one-off decisions around where to eat, what to buy, or which movie to watch [11,25,41]. Nowadays, users expect the recommendation platforms to also support their persistent and overarching interests, including their real-life goals that last days, months or even years [12,29,30]. For example, a user who is into stand-up comedy would want recommendations tailored to their tastes, and might expect the system to help them explore other forms of comedy performance.…”

Section: Introductionmentioning

confidence: 99%

Deconfounding User Satisfaction Estimation from Response Rate Bias

Christakopoulou

Traverse

Potter

et al. 2020

Fourteenth ACM Conference on Recommender Systems

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Improving user satisfaction is at the forefront of industrial recommender systems. While significant progress has been made by utilizing logged implicit data of user-item interactions (i.e., clicks, dwell/watch time, and other user engagement signals), there has been a recent surge of interest in measuring and modeling user satisfaction, as provided by orthogonal data sources. Such data sources typically originate from responses to user satisfaction surveys, which explicitly ask users to rate their experience with the system and/or specific items they have consumed in the recent past. This data can be valuable for measuring and modeling the degree to which a user has had a satisfactory experience on the recommendation platform, since what users do (engagement) does not always align with what users say they want (satisfaction as measured by surveys). We focus on a large-scale industrial system trained on user survey responses to predict user satisfaction. The predictions of the satisfaction model for each user-item pair, combined with the predictions of the other models (e.g., engagement-focused ones), are fed into the ranking component of a real-world recommender system in deciding items to present to the user. It is therefore imperative that the satisfaction model does an equally good job on imputing user satisfaction across slices of users and items, as it would directly impact which items a user is exposed to. However, the data used for training satisfaction models is biased in that users are more likely to respond to a survey when they will respond that they are more satisfied. When the satisfaction survey responses in slices of data with high response rate follow a different distribution than those with low response rate, response rate becomes a confounding factor for user satisfaction estimation. We find positive correlation between response rate and ratings in a large-scale survey dataset collected in our case study. To address this inherent response rate bias in the satisfaction data, we propose an inverse propensity weighting approach within a multi-task learning framework. We extend a simple feed-forward neural network architecture predicting user satisfaction to a shared-bottom multi-task learning architecture with two tasks: the user satisfaction estimation task, and the response rate estimation task. We Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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“…On the other hand, the beneficiaries are the patients. In the latter case, RS focuses on delivering high quality, evidence-based, health-related content to end-user patients, for example suggesting clinical examinations [22], lifestyle changes [23,24] , or improving patient safety [25]. Similarly, they are also used to indicate to the patient a better understanding of his/her personal health status, to retrieve semantically-related content, or to suggest web sites concerning specific diseases [26,27].…”

mentioning

confidence: 99%

Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations

et al. 2020

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Conscious and functional use of online social spaces can support the elderly with mind cognitive impairment (MCI) in their daily routine, not only for systematic monitoring, but to achieve effective targeted engagement. In this sense, although social involvement can be obtained when elder’s experiences, interests, and goals are shared and accepted by the community, an important subsistence for aging depends on the compelling information, users’ co-operation, and resource reliability. Unfortunately, applications aimed at optimizing the information content and the reliability of online users are still missing. Within the SystEm of Nudge theory-based ICT applications for OldeR citizens (SENIOR) project, an advanced social platform will be created in which the elderly with MCI will be involved in “optimized” social communities, where suggestions for general well-being will be recognized as useful by users and shared by care providers. We report the results of our study addressing this issue from a theoretical perspective: we propose a computational problem and a heuristic solution where “expert users” can engage and support the elderly by suggesting available services and facilities for their conditions. The numerical experiments on synthetic data are of interest when considering large communities, which is the most natural situation for online social spaces.

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Recommender system for developing new preferences and goals

Cited by 15 publications

References 33 publications

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Deconfounding User Satisfaction Estimation from Response Rate Bias

Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations

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