One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems

Abstract

Today's recommender systems are criticized for recommending items that are too obvious to arouse users' interest. at's why the recommender systems research community has advocated some "beyond accuracy" evaluation metrics such as novelty, diversity, coverage, and serendipity with the hope of promoting information discovery and sustain users' interest over a long period of time. While bringing in new perspectives, most of these evaluation metrics have not considered individual users' di erence: an open-minded user may favor highly novel or diversi ed recommendations whereas a conservative user's appetite for novelty or diversity may not be that large. In this paper, we developed a model to approximate an individual's curiosity distribution over di erent levels of stimuli guided by the well-known Wundt curve in Psychology. We measured an item's surprise level to assess the stimulation level and whether it is in the range of the user's appetite for stimulus. We then proposed a recommendation system framework that considers both user preference and appetite for stimulus where the curiosity is maximally aroused. Our framework di ers from a typical recommender system in that it leverages human's curiosity to promote intrinsic interest with the system. A series of evaluation experiments have been conducted to show that our framework is able to rank higher the items with not only high ratings but also high response likelihood. e recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to traditional approaches. e personalization factor for assessing the stimulus (surprise) strength further helps the recommender achieve smaller (be er) inter-user similarity.

From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks

“…It essentially strives to combine the merits of intrinsic motivation and encourage spontaneous exploration. Furthermore, different types of curiosity-driven learning (CDL) or curiosity-based learning algorithms have been proposed to perform on the classical AI tasks such as classification [122], recommendation [1], optimization [108], and reinforcement learning [110], where the ultimate objective is to improve the learning efficiency and potentially enable intelligent agents to learn in a human-like way. For example, when agents actively select and learn the interesting data that they are curious about, it substantially reduces training steps, realizes the self-supervised evolution [92], and avoids costly labeling while maintaining similar accuracy [130].…”

Curiosity-driven learning in artificial intelligence and its applications

“…These mechanisms aim to combine the benefits of intrinsic motivation and encourage spontaneous exploration, leading to improved learning efficiency and potentially enabling intelligent agents to learn in a human-like way. Curiosity-driven learning (CDL) or curiosity-based learning algorithms have been proposed to perform classical AI tasks such as classification [58], recommendation [59], optimization [60], and reinforcement learning (RL) [61]. For instance, when agents actively select and learn the interesting data that they are curious about, it can substantially reduce training steps, achieve self-supervised evolution [32], and avoid costly labeling while maintaining similar accuracy [33].…”