Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201

CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation

Dating and romantic relationships not only play a huge role in our personal lives but also collectively influence and shape society. Today, many romantic partnerships originate from the Internet, signifying the importance of technology and the web in modern dating. In this paper, we present a text-based computational approach for estimating the relationship compatibility of two users on social media. Unlike many previous works that propose reciprocal recommender systems for online dating websites, we devise a distant supervision heuristic to obtain real world couples from social platforms such as Twitter. Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users. Intuitively, our approach performs both user profiling and match-making within a unified end-to-end framework. CoupleNet utilizes hierarchical recurrent neural models for learning representations of user profiles and subsequently coupled attention mechanisms to fuse information aggregated from two users. To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem. We benchmark our CoupleNet against several machine learning and deep learning baselines. Experimental results show that our approach outperforms all approaches significantly in terms of precision. Qualitative analysis shows that our model is capable of also producing explainable results to users.Comment: Accepted at ICWSM 201

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

Personalized question-based cybersecurity recommendation systems

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts

Maximizing User Engagement In Short Marketing Campaigns Within An Online Living Lab: A Reinforcement Learning Perspective

ABSTRACT MAXIMIZING USER ENGAGEMENT IN SHORT MARKETING CAMPAIGNS WITHIN AN ONLINE LIVING LAB: A REINFORCEMENT LEARNING PERSPECTIVE by ANIEKAN MICHAEL INI-ABASI August 2021 Advisor: Dr. Ratna Babu Chinnam Major: Industrial & Systems Engineering Degree: Doctor of Philosophy User engagement has emerged as the engine driving online business growth. Many firms have pay incentives tied to engagement and growth metrics. These corporations are turning to recommender systems as the tool of choice in the business of maximizing engagement. LinkedIn reported a 40% higher email response with the introduction of a new recommender system. At Amazon 35% of sales originate from recommendations, while Netflix reports that ‘75% of what people watch is from some sort of recommendation,’ with an estimated business value of $1 billion per year. While the leading companies have been quite successful at harnessing the power of recommenders to boost user engagement across the digital ecosystem, small and medium businesses (SMB) are struggling with declining engagement across many channels as competition for user attention intensifies. The SMBs often lack the technical expertise and big data infrastructure necessary to operationalize recommender systems. The purpose of this study is to explore the methods of building a learning agent that can be used to personalize a persuasive request to maximize user engagement in a data-efficient setting. We frame the task as a sequential decision-making problem, modelled as MDP, and solved using a generalized reinforcement learning (RL) algorithm. We leverage an approach that eliminates or at least greatly reduces the need for massive amounts of training data, thus moving away from a purely data-driven approach. By incorporating domain knowledge from the literature on persuasion into the message composition, we are able to train the RL agent in a sample efficient and operant manner. In our methodology, the RL agent nominates a candidate from a catalog of persuasion principles to drive higher user response and engagement. To enable the effective use of RL in our specific setting, we first build a reduced state space representation by compressing the data using an exponential moving average scheme. A regularized DQN agent is deployed to learn an optimal policy, which is then applied in recommending one (or a combination) of six universal principles most likely to trigger responses from users during the next message cycle. In this study, email messaging is used as the vehicle to deliver persuasion principles to the user. At a time of declining click-through rates with marketing emails, business executives continue to show heightened interest in the email channel owing to higher-than-usual return on investment of$42 for every dollar spent when compared to other marketing channels such as social media. Coupled with the state space transformation, our novel regularized Deep Q-learning (DQN) agent was able to train and perform well based on a few observed users’ responses. First, we explored the average positive effect of using persuasion-based messages in a live email marketing campaign, without deploying a learning algorithm to recommend the influence principles. The selection of persuasion tactics was done heuristically, using only domain knowledge. Our results suggest that embedding certain principles of persuasion in campaign emails can significantly increase user engagement for an online business (and have a positive impact on revenues) without putting pressure on marketing or advertising budgets. During the study, the store had a customer retention rate of 76% and sales grew by a half-million dollars from the three field trials combined. The key assumption was that users are predisposed to respond to certain persuasion principles and learning the right principles to incorporate in the message header or body copy would lead to higher response and engagement. With the hypothesis validated, we set forth to build a DQN agent to recommend candidate actions from a catalog of persuasion principles most likely to drive higher engagement in the next messaging cycle. A simulation and a real live campaign are implemented to verify the proposed methodology. The results demonstrate the agent’s superior performance compared to a human expert and a control baseline by a significant margin (~ up to 300%). As the quest for effective methods and tools to maximize user engagement intensifies, our methodology could help to boost user engagement for struggling SMBs without prohibitive increase in costs, by enabling the targeting of messages (with the right persuasion principle) to the right user

Recommender Systems for Online Dating

Users of large online dating sites are confronted with vast numbers of candidates to browse through and communicate with. To help them in their endeavor and to cope with information overload, recommender systems can be utilized. This thesis introduces reciprocal recommender systems that are aimed towards the domain of online dating. An overview of previously developed methods is presented, and five methods are described in detail, one of which is a novel method developed in this thesis. The five methods are evaluated and compared on a historical data set collected from an online dating website operating in Finland. Additionally, factors influencing the design of online dating recommenders are described, and support for these characteristics are derived from our historical data set and previous research on other data sets. The empirical comparison of the five methods on different recommendation quality criteria shows that no method is overwhelmingly better than the others and that a trade-off need be taken when choosing one for a live system. However, making that trade-off decision is something that warrants future research, as it is not clear how different criteria affect user experience and likelihood of finding a partner in a live online dating context