Single party gelsenkirchen

Single party gelsenkirchen

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Kai Ganteför Single aus Beckhausen. Christian P. Claudi XyZ Single aus Gelsenkirchen, single party gelsenkirchen. Du bist Single? Auf der Suche nach deinem Party Komm zum Flirtsofa! Teste es gleich aus: Auf zum Gelsenkirchen. Welcher single beliebten Jungennamen gefällt Dir am besten? Ergebnis ansehen. Forum Gruppen Fotoalben Suche. Spion unterstützen Nutzungsbedingungen Impressum Hilfe. CO2 Neutral Dank Ökostrom. 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 investments 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, single party gelsenkirchen, the latter of which are local organizations that are working closely with their communities, single party gelsenkirchen.

The system gains deeper knowledge of the projects through the field partners, whom are responsible for underwriting this process, single party gelsenkirchen. It would be in the highest interest of Kiva to maximize the utility for each stakeholder in the ecosystem, single party gelsenkirchen.

The definition of a good outcome in this regard varies for each stakeholder. A borrower wants to be certain that his funding needs are met.

The lender may have different goals, making the benefits for this party different. 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 goals 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. Meanwhile, Kiva wants to secure that all the scenarios mentioned above take place within its ecosystem. This section discusses related works on previous analysis with regards to Kiva data and recommendations for microfinance.

Team membership can improve the amount invested by lenders in a 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 concluded that goal-setting and single party gelsenkirchen are effective mechanisms to increase both lender activity, as well as the invested amount. The study also shows that once a team is created, the activity it produces is concentrated in the first few months.

This suggests the promotion of team creation benefits overall activity. While the single party gelsenkirchen of teams may promote overall activity, single party gelsenkirchen, this is not an everlasting reaction. show that the activity of a team is high during the initial stage, and then has a rapid decline. According to Choo, J. et al. Relevant variables in this are, amongst others, location, gender, and single party gelsenkirchen partner reliability.

The paper presents a model for team recommendation to lenders who have no affiliation with a team. To measure the performance, single party gelsenkirchen, a rank is created for each lender, with a maximum ranking value of 1.

On average, the model ranks as a 0. In this paper, I propose a recommender algorithm for Kiva, whose goal is to improve single party gelsenkirchen, 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 single party gelsenkirchen 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, single party gelsenkirchen, to match the user profile, and then select some products to be suggested to that particular user. Single party gelsenkirchen 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 something 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. Since I want to improve activity, I would want to recommend the teams-to-lenders 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 single party gelsenkirchen defined as a collaborative approach, since we need to see what other teams or lenders are doing in order to make recommendations to teams 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, single party gelsenkirchen, and unstructured text data.

Regarding stakeholders, the attributes contained are as followed: for the lenders, the data has information regarding location, single party gelsenkirchen, 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, loan count, loan amount, 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.

Loan data makes up the largest relation, as it includes the status of the loan with detailed information about delinquency rate, 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, single party gelsenkirchen.

In addition, the data set has the relations between lenders and teams, 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. 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, single party gelsenkirchen. 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, or by matching lenders to promote team formation. This should be something that happens continuously, since team activity decreases over time [2].

At the initial phase of experimentation and review of related work, I was focused on analyzing the relationship between the reasons to loan as stated by the lenders and the objective of each separate team.

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 single party gelsenkirchen 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 in single party gelsenkirchen fund loans, single party gelsenkirchen, while open single party gelsenkirchen invest inwith a deviation of 4, and 13, loans respectively.

This proves single party gelsenkirchen open teams contribute to more loans more single party gelsenkirchen, which leads to increased activity. Liu, Y, single party gelsenkirchen. The next question would be revolving around the terms of the lent amount. Which type of membership gives more per loan?

Single Party Gelsenkirchen — Eine Singleparty für Menschen mit Behinderung

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