What’s going on in my city? Recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

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

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

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

Improved recommendation of photo-taking locations using virtual ratings

We consider the task of collaborative recommendation of photo-taking locations. We use datasets of geotagged photos. We map their locations to a location grid using a geohashing algorithm, resulting in a user x location implicit feedback matrix. Our improvements relative to previous work are twofold. First, we create virtual ratings by spreading users' preferences to neighbouring grid locations. This makes the assumption that users have some preference for locations close to the ones in which they take their photos. These virtual ratings help overcome the discrete nature of the geohashing. Second, we normalize the implicit frequency-based ratings to a 1-5 scale using a method that has been found to be useful in music recommendation algorithms. We demonstrate the advantages of our approach with new experiments that show large increases in hit rate and related metrics

Estimating Error and Bias of Offline Recommender System Evaluation Results

Recommender systems are software applications deployed on the Internet to help people find useful items (e.g. movies, books, music, products) by providing recommendation lists. Before deploying recommender systems online, researchers and practitioners generally conduct offline evaluations to compare the accuracy of top- recommendation lists among candidate algorithms using users’ history consumption data. These offline evaluations typically use metrics and methodologies borrowed from machine learning and information retrieval and have several well-known biases that affect the validity of their results, including popularity bias and other biases arising from the missing-not-at-random nature of the data used. The existence of these biases is well-established, but their extent and impact are not as well-studied. In this work, we employ controlled simulations with varying assumptions about the distribution and structure of users’ preferences and the rating process to estimate the distributions of the errors in recommender experiment outcomes as a result of these biases. We calibrate our simulated datasets to mimic key statistics of existing public datasets in different domains and use the simulated data to assess the error in estimating true accuracy with observable rating data. We find inconsistency of the evaluation metric scores and the order in which they rank recommendation algorithms in the synthetic true preference and the observation dataset. Simulation results show that offline evaluations are sometimes fooled by intrinsic effects in the data generation process into mistakenly ranking algorithms. The extent of this effect is sensitive to assumptions