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Dating Hessen : Singles aus Hessen
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Kiva is a non-profit organization which helps entrepreneurs get financing from common people across the world. By constructing an ecosystem in which borrowers, lenders and supporters come together, Kiva provides resources for small projects. In order make the ecosystem available to as many people as possible, small amount singletreffen hessen are available for anyone to fund and help others start their projects.
The minimum investment needed to participate is 25 American dollars. Kiva has created partnerships with other non-profit organizations and microfinance institutions, the latter of which are local organizations that are working closely with their communities. The system gains deeper knowledge of the projects through the field partners, whom are responsible for underwriting this process.
It would be in the highest interest of Kiva to maximize the utility for each stakeholder in the ecosystem. The definition of a singletreffen hessen outcome in this regard varies for each stakeholder.
A borrower wants to be certain that his funding needs are met. Singletreffen hessen lender may have different goals, making the benefits for this party different, singletreffen hessen. It is likely that certain lenders want to lend to as many people as possible. In these cases, good repayment rates would help, so that these persons can continue lending, since resources are limited.
There may be other singletreffen hessen for a lender, like wanting to support specific countries or activities, while others may wish to fund one project at a time. Among field partners, there may be different ways in which benefits are perceived, too. Some partners may want to be able to help both borrowers as well as lenders, as to be able to connect with one another.
For those who fund the borrower in advance, the main concern would be concentrated on getting the funding from Kiva, singletreffen hessen.
Meanwhile, Kiva wants to secure that all the singletreffen hessen mentioned above take place within its ecosystem. Singletreffen hessen section discusses related works on previous analysis with regards to Kiva data and recommendations for microfinance, singletreffen hessen.
Team membership can improve the amount invested by lenders in singletreffen hessen significant way, but does not affect the frequency in which a lender is actively funding projects [1]. Chen, Roy et al.
Working with Kiva, they implemented a random test, consisting of 22, experiments. The authors singletreffen hessen that goal-setting and coordination are singletreffen hessen mechanisms to increase both lender activity, as well as the invested amount. The study also shows that once a team is created, singletreffen hessen, the activity it produces is concentrated in the first few months. This suggests the promotion of team creation benefits overall activity.
While the forming of teams may promote overall singletreffen hessen, this is not an everlasting reaction. show that the activity of a team is high during the initial stage, singletreffen hessen, and then has a rapid decline. According to Choo, J. et al. Relevant variables in this are, amongst others, location, gender, and field partner reliability.
The paper presents a model for team recommendation to lenders who have no affiliation with singletreffen hessen team. To measure the performance, a rank is created for each lender, with a maximum ranking value of 1. On average, singletreffen hessen, the model ranks as a 0.
In this paper, I propose a recommender algorithm for Kiva, whose goal is to improve activity, by recommending teams to lenders. There are two main approaches to recommender systems: content-based filtering and collaborative filtering. Content-based approaches use data created within a system, as to be able to provide recommendations for its users. If the system handles products, then it would take information about the product, such as category, price, color, brand and more, to match the user profile, and then select some products to be suggested to that particular user.
The collaborative filtering method uses a similar mechanism between users, herewith suggesting products to each user. In a system where two users are similar because they, for example, both liked similar movies the system would suggest singletreffen hessen to one user, based on the information of what the second user has seen and liked.
In the Kiva space there are three types of possible recommendations: loans-to-lenders, loan-to-teams and teams-to-lenders. All three recommendation approaches are possible, singletreffen hessen.
Since I want to improve activity, I would singletreffen hessen to singletreffen hessen the singletreffen hessen or loans-to-teams. Teams are formed by users, so the first recommendation would be a content-based approach. The loan-to-teams recommendation can be defined as a collaborative approach, since we need to see what other teams or lenders are doing in order to make recommendations to singletreffen hessen or lenders.
Another way to improve activity is to suggest the formation of new teams. To accomplish that, I would use natural language processing to match users with teams. The attributes within the relations include geo-spatial, categorical, continuous, and unstructured text data. Regarding stakeholders, the attributes contained are as followed: for the lenders, the data has information regarding location, occupation, sign up date, and loan count, as well as information on the number of loans funded by the user, its invitee count, and the number of invitations sent to other users to fund a loan, because the latter is one of the reasons to be a part of Kiva.
The team data has category selected from a list of options provided by the system, described as free text loan, because this is a brief description of the overall team goal, singletreffen hessen, loan count, loan amount, singletreffen hessen, member count, membership type open or closeddate of creation and location.
There are no restrictions to join a team with regards to location, but it helps to find affinities: when a new user would like to join a team, the region he or she is in could become one of the first reasons to join.
Singletreffen hessen data makes up the largest relation, as it includes the status of the loan with detailed information about delinquency rate, singletreffen hessen, repayment status, sector and more. Activity is a sub sector type of attribute, loan use as a free text to state the purpose of the loan, location, currency and amount.
In addition, singletreffen hessen, the data set has the relations between lenders and teams, singletreffen hessen, lenders to loan, which are many-to-many. A lender is not required to have a team affiliation, nor is he restricted to join only one team, singletreffen hessen.
There may be lenders that have joined several teams. The two main relationships I am interested in are:. General statistics of the datasets are compared in Figure 1. Teams are very important in the Kiva ecosystem. I know that promoting team creation improves activity, leading to more funding with more frequency.
Kiva would benefit from recommending teams to users that have never joined a team, singletreffen hessen, or by matching lenders to promote team formation. This should be something that happens continuously, since singletreffen hessen activity decreases over time [2]. At the initial phase of experimentation and review of related work, I was focused on analyzing the singletreffen hessen between the reasons to loan as stated by the singletreffen hessen and the objective of each separate singletreffen hessen. To investigate this, I would only concentrate my research on lenders and teams that have stated their reason to loan.
Taking that constrain into consideration, the team data gathered from the Kiva API corresponds with the data of teams created within the same time space as the lenders in the dataset. There are 11, teams within that space, making up a total of On average, a team has 32 members, with a standard deviation of and a median of 4 members.
Figure 2 shows team logarithmic distribution by member count. There are two types of teams: those that are open for anyone to join, and those that require prospective members to be approved by the administrators. Each type is thus identified as either open or closed.
Which type of team membership contributes more to Kiva? Closed teams singletreffen hessen average fund loans, while open teams invest inwith a deviation of 4, and 13, loans respectively. This proves that open teams contribute to more loans more often, which leads to increased activity. Liu, Y. The next question would be revolving around the terms of the lent amount.
Which type of membership gives more per loan? Since the data does not reveal how much each lender gives with each loan, singletreffen hessen, most Kiva-related papers make the assumption that each lender provided an equal amount to each loan. That is the same amount lent as for teams of the closed membership type. In terms of the amount lent, there is no visible difference in the amount derived by membership type.
This leads us to the same findings as other related work. There is a significant difference in the activity each type contributes to the ecosystem.
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