Reinforcement Sensitivity and Engagement in Proactive Recommendations: Experimental Evidence

Rook, Laurens; Sabic, Adem; Zanker, Markus

doi:10.1007/978-3-319-67431-5_2

Cited by 1 publication

(1 citation statement)

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“…For example, The emerging field of NeuroIS 10 employs sensors and methodologies from neuroscience in order to more accurately understand the cognitive processes of users when interacting with technology and information systems. For instance, Rook et al (2018) observed that users with specific personality traits (such as an anxiety-related behavioral inhibition) tend to be more engaged when confronted with proactive recommendations and that the accuracy of provided recommendations moderates this effect, i.e., inaccurate recommendations make them ruminate about them.…”

Section: The Role Of User Studiesmentioning

confidence: 99%

Collaborative Filtering: Matrix Completion and Session-Based Recommendation Tasks

Jannach¹,

Zanker²

2018

Collaborative Recommendations

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This chapter provides a self-contained overview on the basics of collaborative filtering recommender systems. It covers two main classes of recommendation scenarios. In the classical matrix completion problem formulation, the task of an algorithm is to make longer-term relevance predictions given a user-item rating matrix. In session-based recommendation scenarios, the goal is to predict relevant items given a user's observed short-term behavior. From an algorithmic perspective, the chapter particularly focuses on neighborhood-based methods, which were proposed in the early days of collaborative filtering and which are still relevant today. The chapter addresses the entire life-cycle of algorithm development and also discusses libraries, datasets, and implementation aspects. Furthermore, it covers evaluation issues and reflects on today's research methodology in the field. Overall, the chapter shall serve as a starting point for readers, providing pointers to more detailed discussion of the various aspects regarding the design and evaluation of collaborative filtering recommender systems.

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Section: The Role Of User Studiesmentioning

confidence: 99%