Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Reciprocal Recommender and its Application in Online Recruiting System

推荐系统作为信息过载的有效解决方案，正在越来越多的领域得到了应用，其在电子商务领域取得的巨大成功引起了产业界和学术界的关注。传统的推荐系统仅考虑单方面的意向偏好，其用户建模方法和推荐算法无法适用于目前应用越来越广泛的基于双向意向偏好的推荐系统中。求职招聘推荐是双向推荐的典型应用场景，求职者和企业人力资源经理双方都是具有主观意向偏好的主体，单方面地考虑一方面的意向偏好将导致推荐的失败。 本文以双向推荐系统和面向求职招聘领域的双向推荐系统应用为研究对象，主要工作包括以下几个方面： （1）双向推荐系统的定义与特性分析。定义双向推荐系统，从用户场景、算法适用性两个方面对比分析传统推荐系统和双向推荐...Recommender system has been seen as one of the most effective solutions to the problem of information overload, and it has been applied to more and more area. The great success of recommender system in the area of E-commerce has attracted a lot attention from people from both the industry and academia. Most traditional recommender systems try to recommender items only considering the preference of...学位：工学硕士院系专业：航空航天学院_模式识别与智能系统学号：2322013115333

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

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

Talent recommendation system in big data era

1人才推荐系统的应用背