3rd Workshop on Emotions and Personality in Personalized Systems 2015, in conjunction with the ACM RecSys 2015 conference in Vienna (Austria) on 19. September 2015
Presentation: Personality in Recommender Systems
"The personality-based recommender systems (RS) has emerged as a new type of RS in recent years, given that personality contains valuable information enabling systems to better understand users' preferences. This presentation first gives an overview of the state-of-the-art in this area, including the approaches developed for enhancing collaborative filtering (CF) by computing users' or items' personality similarity, as well as the one that incorporates personality into matrix factorization to predict items that users are able to rate for active learning.
We then discuss several open issues. One issue is how to utilize personality to improve recommendation diversity. Diversity refers to the system's ability in returning different items in one set, which may help users more effectively explore the product space and discover unexpected items. Our recent studies identified the effect of personality on users' diversity differences, and demonstrated that people perceive the system, which considers personality in adjusting recommendations' diversity degree, more competent and satisfying.
We also show how to acquire personality through unobtrusive and implicit way, so as to save users' efforts in answering personality quizzes. Through testing an inference model in movie domain that unifies both types of domain-dependent and -independent features for deriving users' personality from their behavior, we proved that the implicitly inferred personality can also be helpful to augment the system's recommendation accuracy.
Other open issues include how to develop personality-based cross domain RS for addressing the critical cold-start problem, how to exploit the influence of personality on users' emotions for boosting context-aware RS, and how to elicit more domain-independent features for generalizing the personality inference procedure."
- A Multimodal Framework for Recognizing Emotional Feedback in Conversational Recommender Systems
- Discount Sensitive Recommender System for Retail Business
- Predicting Personality Traits with Instagram Pictures
- A General Architecture for an Emotion-aware Content-based Recommender System
- A Multimodal System For Nonverbal Human Feature Recognition In Emotional Framework
- Emotion in Consumer Simulations for the Development and Testing of Recommendations for Marketing Strategies
PAPER On the Role of Personality Traits in Followee Recommendation Algorithms
Which is the RIGHT approach to innovate in the Personality Based Recommender Systems Arena?
The same approach to innovate in the Online Dating Industry == 16PF5 test or similar to assess personality traits and a new method to calculate similarity between quantized patterns. Oh that is exactly ............ guess ............. yes ........ LIFEPROJECT METHOD, ready since 2001!
The next wave of innovation!
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.
Without offering the NORMATIVE16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry.
All other proposals are NOISE and perform as placebo.
Please read: The 8 tips to innovate in the Online Dating Industry!
The Online Dating Industry does not need a 10% improvement, a 50% improvement or a 100% improvement. It does need "a 100 times better improvement", not 100% better, 100X better The Online Dating Industry needs a very powerful algorithm like the "Teller Ulam design". In this case 100 times more powerful than actual matching algorithms.