Panagiotis Adamopoulos

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system - the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.

Beyond rating prediction accuracy: on new perspectives in recommender systems

This paper proposes a number of studies in order to move recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored paradigms and also propose new approaches aiming at more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. In particular, we move our focus from even more accurate rating predictions and aim at offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems

This paper proposes a novel method for estimating unknown ratings and recommendation opportunities and illustrates the practical implementation of the proposed approach by presenting a certain variation of the classical k-NN method in neighborhood-based collaborative filtering systems using weighted percentiles. We conduct an empirical study showing that the proposed method outperforms the standard user-based collaborative filtering approach by a wide margin in terms of item prediction accuracy and utility-based ranking metrics across various experimental settings. We also demonstrate that this performance improvement is not achieved at the expense of other popular performance measures, such as catalog coverage and aggregate diversity. The proposed approach can also be applied to other popular methods for rating estimation.

On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems

This paper proposes a number of studies in order to move the field of recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored recommendation strategies and propose new approaches targeting to more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. The overall goal of this research program is to expand our focus from even more accurate rating predictions toward a more holistic experience for the users, by providing them with non-obvious but high quality recommendations and avoiding the over-specialization and concentration bias problems. In particular, we propose a new probabilistic neighborhood-based method as an improvement of the standard

REDD 2014 -- international workshop on recommender systems evaluation: dimensions and design

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The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms

-nearest neighbors approach, alleviating some of the most common problems of collaborative filtering recommender systems, based on classical metrics of dispersion and diversity as well as some newly proposed metrics. Furthermore, we propose a concept of unexpectedness in recommender systems and operationalize it by suggesting various mechanisms for specifying the expectations of the users and proposing a recommendation method for providing the users with unexpected but high quality personalized recommendations that fairly match their interests. Besides, in order to generate utility-based recommendations for Massive Open Online Courses (MOOCs) that better serve the educational needs of students, we study the satisfaction of users with online courses vis-a-vis student retention. Finally, we summarize the conclusions of the conducted studies, discuss the limitations of our work and also outline the managerial implications of the proposed stream of research.

WHAT MAKES A GREAT MOOC? AN INTERDISCIPLINARY ANALYSIS OF STUDENT RETENTION IN ONLINE COURSES

Evaluation is a cardinal issue in recommender systems; as in any technical discipline, it highlights to a large extent the problems that need to be solved by the field and, hence, leads the way for algorithmic research and development in the community. Yet, in the field of recommender systems, there still exists considerable disparity in evaluation methods, metrics and experimental designs, as well as a significant mismatch between evaluation methods in the lab and what constitutes an effective recommendation for real users and businesses. Even after the relevant quality dimensions have been defined, a clear evaluation protocol should be specified in detail and agreed upon, allowing for the comparison of results and experiments conducted by different authors. This would enable any contribution to the same problem to be incremental and add up on top of previous work, rather than grow sideways. The REDD 2014 workshop seeks to provide an informal forum to tackle such issues and to move towards better understood and shared evaluation methodologies, allowing one to leverage the efforts and the workforce of the academic community towards meaningful and relevant directions in real-world developments.

On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems

Word of mouth (WOM) plays an increasingly important role in shaping consumers’ behavior and preferences. In this paper, we examine whether latent personality traits of online users accentuate or attenuate the effectiveness of WOM in social media platforms. To answer this question, we leverage machine-learning methods in combination with econometric techniques utilizing a novel quasi-experiment. Our analysis yields two main results. First, there is a positive and statistically significant effect of the level of personality similarity between two social media users on the likelihood of a subsequent purchase from a recipient of a WOM message after exposure to the WOM message of the sender. In particular, exposure to WOM messages from similar users in terms of personality, rather than dissimilar users, increases the likelihood of a postpurchase by 47.58%. Second, there are statistically significant effects of specific pairwise combinations of personality characteristics of senders and recipients of WOM messag...