Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 170, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.114382
Keywords
Recommender Systems; Affective Computing; Music Recommendation; Decision Making; myPersonality; Emotions
Categories
Funding
- Apulia Region, Italy, POR Puglia FESR-FSE 2014-2020 Innonetwork, project DECiSION: DATA-DRIVEN CUSTOMER SERVICE INNOVATION
Ask authors/readers for more resources
Decision making is a cognitive process where alternatives are identified and chosen based on preferences, beliefs, and importance given by the decision maker. Recommender systems assist people in making choices by reducing the space of possible options, but for better support, they should consider a holistic view of user behavior, including how emotions and personality traits influence decision-making patterns.
Decision making is the cognitive process of identifying and choosing alternatives based on preferences, beliefs, and degree of importance given by the decision maker to objects or actions. For instance, choosing which movie to watch is a simple, small-sized decision-making process. Recommender systems help people to make this kind of choices, usually by computing a short list of suggestions that reduces the space of possible options. These systems are strongly based on the knowledge of user preferences but, in order to fully support people, they should be grounded on a holistic view of the user behavior, that includes also how emotions, mood, and personality traits influence her choosing patterns. In this work, we investigate how to include emotional aspects in the recommendation process. We suggest that the affective state of the user, defined by a set of emotions (e.g., joy, surprise), constitutes part of choosing situation that should be taken into account when modeling user preferences. The main contribution of the paper is a general emotion-aware computational model based on affective user profiles in which each preference, such as a 5-star rating on a movie, is associated with the affective state felt by the user at the time when that preference was collected. The model estimates whether an unseen item is suitable for the current affective state of the user, by computing an affective coherence score that takes into account both the affective user profile and not-affective item features. The approach has been implemented into an Emotion-aware Music Recommender System, whose effectiveness has been assessed by performing in-vitro experiments on two benchmark datasets. The main outcome is that our system showed improved accuracy of recommendations compared to baselines which include no affective information in the recommendation model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available