One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems
This work addresses the issue of sustaining user interest in recommender systems by personalizing novelty and diversity, though it is incremental as it builds on existing 'beyond accuracy' metrics.
The paper tackled the problem of recommender systems recommending items that are too obvious by modeling users' personal curiosity based on the Wundt curve from Psychology, resulting in a framework that ranks items with higher ratings and response likelihood, and achieves smaller inter-user similarity compared to traditional approaches.
Today's recommender systems are criticized for recommending items that are too obvious to arouse users' interest. That'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' difference: an open-minded user may favor highly novel or diversified 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 different 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 differs 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. The recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to traditional approaches. The personalization factor for assessing the stimulus (surprise) strength further helps the recommender achieve smaller (better) inter-user similarity.