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

Can deep learning help you find the perfect match?

Is he/she my type or not? The answer to this question depends on the personal preferences of the one asking it. The individual process of obtaining a full answer may generally be difficult and time consuming, but often an approximate answer can be obtained simply by looking at a photo of the potential match. Such approximate answers based on visual cues can be produced in a fraction of a second, a phenomenon that has led to a series of recently successful dating apps in which users rate others positively or negatively using primarily a single photo. In this paper we explore using convolutional networks to create a model of an individual's personal preferences based on rated photos. This introduced task is difficult due to the large number of variations in profile pictures and the noise in attractiveness labels. Toward this task we collect a dataset comprised of $9364$ pictures and binary labels for each. We compare performance of convolutional models trained in three ways: first directly on the collected dataset, second with features transferred from a network trained to predict gender, and third with features transferred from a network trained on ImageNet. Our findings show that ImageNet features transfer best, producing a model that attains $68.1\%$ accuracy on the test set and is moderately successful at predicting matches

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line

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

Online Reciprocal Recommendation with Theoretical Performance Guarantees

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

Sur l'utilisation de réseaux de neurones dans un système de recommandations réciproques

Le domaine de la recommandation de personne à personne comporte de multiples applications, mais il demeure moins bien étudié que celui de la recommandation de produits ou de services. Contrairement à la recommandation d’items, la recommandation de personnes doit tenir compte de la possibilité que la recommandation ne plaise pas nécessairement dans les deux sens, ce qui impose des difficultés supplémentaires et augmente la complexité. Dans les dernières années, les algorithmes à base de réseaux neuronaux ont su tirer parti des complexités présentes dans des domaines aussi divers que la vision informatique, le traitement du langage naturel et de la parole, et la génération d’images. Bien que leur utilisation pour les systèmes de recommandations en général ait été étudiée, l’utilisation des réseaux neuronaux est encore peu ou pas explorée dans le domaine de la recommandation de personne à personne. Nous explorons cette avenue. Nous avons utilisé des données provenant d’une plateforme qui permet de connecter deux personnes voulant apprendre l’une de l’autre. Cette base de données possède à la fois des données implicites sous la forme de vues de profils, de messages ou de rencontres, et des données explicites, les descriptions des offres et demandes ainsi que des mots-clés. L’algorithme créé pour faire les recommandations avec ces données est une combinaison de réseaux de neurones récurrents. Ceux-ci sont entraînés en essayant de prédire les données implicites à partir des données explicites. L’algorithme est comparé à l’approche classique d’analyse sémantique latente (Latent Semantic Analysis) sur la base des mesures de précision, rappel et score F1. Les résultats montrent que le nouvel algorithme prédit moins bien les données historiques lorsque peu de prédictions sont faites, mais que la qualité de celles-ci augmente plus vite avec le nombre de prédictions que le modèle comparé. Ceci se traduit par une meilleure précision lorsque le rappel est grand. Ce résultat est similaire lorsque les deux modèles sont augmentés d’une approche par filtres collaboratifs, bien que la différence s’amoindrit. L’utilisation d’agrégation des expertises par maximum ou moyenne ne semble pas avoir beaucoup d’effet pour l’un ou l’autre des modèles. Ce mémoire introduit une nouvelle approche pour les systèmes de recommandations permettant d’entraîner des modèles utilisant des données dépendant uniquement de l’utilisateur pour faire des recommandations aussi complexes et diversifiées que celles faites par les filtres collaboratifs.----------ABSTRACT: People-to-people recommandations is a relatively new domain of study compared to recommender systems in general. Contrary to recommender systems where items are recommended to people, the recommendation of people has to take into account the fact that recommendations may be not be as good in the reversed direction. This factor increases the complexity of the recommendation. During the last few years, algortithms based on neural networks have been able to find patterns in domains as diverse as computer vision, natural language and speech processing, and image generation. While their use in recommender systems in general has been studied, work on this topic is still in its infancy and we find no contribution yet for people-to-people recommendation. We explore this avenue and use data from a platform where two people wanting to learn from another can connect. This dataset contains implicit data in the form of profile views, messages and meetings, as well as explicit data under the format of demand and offer textual descriptions and tags. The people-to-people recommender system developed is a neural network based on recurrent neurons and trained by trying to predict the implicit data from the explicit data. It is compared to the classical Latent Semantic Analysis approach based on precision, recall and F1 score metrics. The results show that the new algorithm does not do as well a job predicting historical data when few predictions are made, but that the quality goes up more quickly with the number of predictions made than the compared model. This is shown by a better precision when recall is large. The result is similar when both models are augmented with collaborative filters, but with a smaller difference between them. The use of different pooling methods by maximum or mean doesn’t seem to have much of an effect on either model. This model introduces a new approach for recommender systems that enables them to use data depending only on the user to make recommendations as complex and diversified as those made by collaborative filters