Contexts in source publication

Context 1
... only consider those who sent between 20 to 30 likes, which is approximately the average number of likes in the data. 19 The results are summarized in Figure 1, and the implementation details are gathered in Appendix A. Since our algorithms are modular, we can investigate the prediction power of each step. We consider the following recommenders: ...
Context 2
... MF: for each m, choose women who correspond to the N highest fitted value p ′ m q w estimated by the procedure described in section 4. We begin with the OLS and the MF recommender as the baseline cases and then add other modules, such as the pseudo market and the CS model. The average hit rate under different N , the length of the recommendation list, can be found in Figure 1. We use triangles to label the 19 The extreme cases in which men liked an exceedingly small or large number of women will lead to a hit rate that is close to zero or one, regardless of the algorithm. ...

Similar publications

Chapter
Full-text available
Smart parking models and approaches aim to optimize the use of parking spaces, reduce traffic congestion, and provide a more efficient parking experience. There are various models, and approaches are used like sensor-based parking, IoT-based parking, mobile-based parking, automatic parking systems, and shared parking. Overall, these models and appr...
Conference Paper
Full-text available
Transmission grid congestion is one of the obstacles of renewable energy integration. It is therefore important to understand the causes and consequences of congestion. This paper describes three published data sets that contain pivotal information about transmission grid congestion and two countermeasures, that are typically used in uniform price...
Article
Full-text available
The internet of things (IOT) concept has evolved into one of pillars of the new technologies sector. Artificial intelligence is added to IOT systems because it is a best solution to manage huge data flows and storage on IOT. In IOT the data flows internets will have sensor data and user data that send and receive from workstations. Due to increase...
Article
Full-text available
Short-term locational marginal price (LMP) forecasting is the traditional problem of market participants and other institutions maximizing their profit. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have t...

Citations

... One important aspect of the job market is the competition between job seekers for jobs and between jobs (or the companies) for hiring job seekers [9]. This competition may be amplified by the presence of congestion, meaning that some items are more visible or desirable than many others [10]. In the context of job recommendation, congestion refers to recommending few job positions to many job seekers, while other job positions are recommended to none or few job seekers. ...
... This competition in the job market is amplified by the problem of congestion in recommendations. This problem has been studied in the literature by under different names such as aggregate diversity [14], [15], sales diversity [15], [16], longtail recommendation [17], fairness of exposure [18], and congestion [10], [11]. There are also some related topics to this paper such as considering position bias [19], and maximizing social welfare in proactive (application) and reactive (evaluation) scenarios [20], which are not the focus of this paper. ...
... As suggested by Bied et al. [11], Congestion is divided by log(|I|) (I represents the set of items) to map it to [−1, 0]. Equation 10 shows Congestion@k. Congestion is minimized if the entropy of the market shares is minimized. ...
Article
Full-text available
Recommender systems often face congestion, characterized by an uneven distribution in the frequency of item recommendations. The presence of congestion in recommendations is especially problematic in domains where users or items have limited availability. For example, recommending one vacancy to many job seekers results in frustration of job seekers and job market inefficiency. We propose a novel in-processing approach to job recommendation called ReCon, accounting for the congestion problem. Our approach is to use an optimal transport component to ensure a more equal spread of vacancies over job seekers, combined with a job recommendation model in a multi-objective optimization problem. Moreover, we propose a scalable solution so that ReCon is applicable to large-scale datasets. We evaluated our approach on several real-world job market datasets. The evaluation results show that ReCon has good performance on both congestion-related (e.g., Congestion, Coverage, and Gini Index) and desirability (e.g., NDCG, Recall, and Hit Rate) measures. In most cases, ReCon is Pareto optimal for some selections of hyper-parameters in comparison to the baselines.
... that some items are more visible and desirable than others [5]. The congestion problem is illustrated with an example in Figure 1. ...
... The same conclusion could generally be established for all the desirability measures against all congestion-related measures, where for some selections of hyper-parameters, ReCon usually finds a good trade-off between both measures. More figures for different combinations of measures are available in the online supplementary material 5 . ...
... To achieve this goal, the job recommendation scores were adjusted using a dynamic forecasting model that penalized or boosted scores based on the predicted number of applications. Chen et al. [5] use a decentralized economic model to learn the scoring functions and use an optimal transport approach for the final recommendations to reduce congestion in the recommendations. ...
Preprint
Full-text available
Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended. Some items may be recommended much more than others. Recommenders are increasingly used in domains where items have limited availability, such as the job market, where congestion is especially problematic: Recommending a vacancy -- for which typically only one person will be hired -- to a large number of job seekers may lead to frustration for job seekers, as they may be applying for jobs where they are not hired. This may also leave vacancies unfilled and result in job market inefficiency. We propose a novel approach to job recommendation called ReCon, accounting for the congestion problem. Our approach is to use an optimal transport component to ensure a more equal spread of vacancies over job seekers, combined with a job recommendation model in a multi-objective optimization problem. We evaluated our approach on two real-world job market datasets. The evaluation results show that ReCon has good performance on both congestion-related (e.g., Congestion) and desirability (e.g., NDCG) measures.
... However, these studies do not take into account the preferences of the agents while proposing the recommendation strategy. One of the problems that come along with recommendation systems and their abstraction with matching markets [45,12] is the gap between the utility of the agents and the regret that comes with the optimization objective of the recommendation systems. To this end, ...
Preprint
Full-text available
Sequential fundraising in two sided online platforms enable peer to peer lending by sequentially bringing potential contributors, each of whose decisions impact other contributors in the market. However, understanding the dynamics of sequential contributions in online platforms for peer lending has been an open ended research question. The centralized investment mechanism in these platforms makes it difficult to understand the implicit competition that borrowers face from a single lender at any point in time. Matching markets are a model of pairing agents where the preferences of agents from both sides in terms of their preferred pairing for transactions can allow to decentralize the market. We study investment designs in two sided platforms using matching markets when the investors or lenders also face restrictions on the investments based on borrower preferences. This situation creates an implicit competition among the lenders in addition to the existing borrower competition, especially when the lenders are uncertain about their standing in the market and thereby the probability of their investments being accepted or the borrower loan requests for projects reaching the reserve price. We devise a technique based on sequential decision making that allows the lenders to adjust their choices based on the dynamics of uncertainty from competition over time. We simulate two sided market matchings in a sequential decision framework and show the dynamics of the lender regret amassed compared to the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.