Using Shortlists to Support Decision Making and Improve Recommender System Performance

Schnabel, Tobias; Bennett, Paul; Dumais, Susan T.; Joachims, Thorsten

doi:10.1145/2872427.2883012

Cited by 26 publications

(15 citation statements)

References 29 publications

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“…For example, it is known that people prefer extra motor effort over cognitive effort [13]. As an even more concrete example, it has been observed that this makes people more likely to memorize items with a button click in recommender systems, rather than memorizing the items themselves [36]. There are also more specialized cost-benefit models [4].…”

Section: Information Foragingmentioning

confidence: 99%

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Schnabel

Bennett

Joachims

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data-and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience. CCS CONCEPTS • Information systems → Recommender systems; • Humancentered computing → Interaction design theory, concepts and paradigms; User models; • Computing methodologies → Learning from implicit feedback;

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Section: Information Foragingmentioning

confidence: 99%

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Schnabel

Bennett

Joachims

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Self Cite

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“…The discounted items are supplied to the users in an effort to expand the client's curiosity. [23] examine waitlists as an interface part for recommender frameworks with the double objective of supporting the client's choice procedure, and besides enhancing understood input elicitation for expanded proposal quality. A waitlist is a brief rundown of competitors that the client is as of now considering, e.g., a rundown of a couple of motion pictures the client is at present considering for review.…”

Section: Literature Surveymentioning

confidence: 99%

Untitled

2019

IJCSE

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The recommender systems are the technique of processing and evaluation of user behavioral data in order to suggest the resources according to user needs. In e-learning system, it is actually used to suggest resources and relevant contents to learners. Thus e-Learning recommender system is a technique to identify requirements of learner to provide relevant learning content. In this paper current state-of-art for the e-learning recommendation system is reviewed. In addition of that recent research work in the direction of enhancing the user's experience in online learning is also reported.

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“…Increased privacy concerns will reduce privacy disclosure, which will lower the prediction accuracy of social sensing such that the personalization does not satisfy users’ needs at a basic level. Although the quality of a personalization improves as the volume of available personal data increases [26], users generally do not agree to this data collection [27] since there are many reports of unwanted exposure of information and illegal disclosure to third parties [28]. However, users may disclose their long-term privacy for temporary benefits, such as when filling in questionnaires in exchange for coupons and discounts.…”

Section: Related Workmentioning

confidence: 99%

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

Zhang

2018

Sensors

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Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

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Using Shortlists to Support Decision Making and Improve Recommender System Performance

Cited by 26 publications

References 29 publications

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory

Untitled

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

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