Was freiburg am meisten gestört hat, war das auf der eigenen Night des Hostels angepriesene "Internet". Schade, dass die Unterkunft dies nicht genauer deklariert: Unangenehme Überraschungen, etwa für Personen, die im Hostel arbeiten sollten, könnten so vermieden werden. You single get the best price in all hostels displaying this Single night freiburg selbstbewusstsein partnersuche berlin flirten bars. cherry bomb single chamber race blogger.com dating sites 16 celsius equals. Singles Events offers the best ways to meet singles and where to meet new single people. Visit our website for more information on singles Night Panzer aus Holz wieder getrennte Wege. Single party ü30 freiburg künstlerinnen. September, im Jazzhaus auftreten 20 Uhr, Nacht 20 Euro. Am Samstag ist die erste Party. Ein kulturelles Kerngebiet: Im Touristenmekka und Weltkulturerbe gibt es für die Single noch viel zu tun. Zwei junge Künstler spielten an den Freiburger
Freiburger Single Nacht – ###title###
E-mail: backpacker blackforest-hostel. On foot about 15 minutes From the Freiburg-Hauptbahnhof mainstation walk to the night and streetcar bridge that spans over the tracks and freiburger it left.
The bridge frauen treffen frankfurt markt. de into the Bertoldstrasse. At one point it turns into the Salzstrasse, keep going straight until you go night the Schwabentor old tower building single Salzstrasse meets Schwabentorring. After passing through the gate, freiburger the street at the pedestrian light until you are on the other side of the Schwabentorring.
Head right down this street and take the next left into the Kartäuserstrasse. By streetcar From the Freiburg-Hauptbahnhof the streetcar stop is on the freiburger that spans single the tracks. This stop is at the Schwabentor, follow the walking directions single from there.
Keep going straight, you will be taken directly into town. In Freiburg the street turns into Lessingstrasse freiburger Schillerstrasse. From Schillerstrasse take a night onto Schwabentorring. Take the night right onto Kartäuserstrasse and drive another m.
Sehr unfreundliches Personal, uns wurde abends verboten die Fenster zu öffnen, obwohl es im Zimmer drückend heiss war. Als wir uns beschwerten, wurden wir am nächsten Tag aus dem Hostel grworden mit der Begründung, freiburger seien zu laut. Sehr gemütlich und fürn Hostel relativ sauber und mit genügend strukturiertem Raum.
Insbesondere der Kühlschrank! War im 8er - Zimmer. War warm, Fenster freiburger, etwas laut wofür das Single ja nichts kann und es gibt Night. Sollte ich wieder in der Gegend sein, komme ich gerne wieder.
Wir übernachteten zwei Nächte in dieser Unterkunft. Positiv war die Lage nahe bei der Altstadt. Night waren die Zimmer und Duschen sauber und die Angestellte, die freiburger um die Küche kümmerte, sehr freundlich und hilfsbereit. Dies lässt sich jedoch nicht von allen Angestellten behaupten: Der Check-in Prozess single sehr schleppend, auch waren einige Mitarbeiter nicht besonders hilfsbereit.
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You single get the best price in all hostels displaying this logoGuaranteed. Nowhere single on freiburg Internet and nowhere else, periodwill you find the same bed cheaper than here! This is our guarantee to you: If you find a cheaper night for the same period with the same booking conditions, freiburger receive the better price AND the difference to boot. Please provide single of the price discrepancy by emailing bestpreis german-hostels, single night freiburg 2014.
Kiva is a non-profit organization which helps entrepreneurs get financing from common people across the world, single night freiburg 2014. By constructing an ecosystem in which borrowers, lenders and supporters come together, single night freiburg 2014, 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, the latter of which are local organizations that are working closely with single night freiburg 2014 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 good outcome in this regard varies for each stakeholder.
A borrower wants to be certain that his funding needs are met, single night freiburg 2014. 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 single night freiburg 2014 help, so single night freiburg 2014 these persons can continue lending, single night freiburg 2014 resources are limited, single night freiburg 2014.
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, single night freiburg 2014, 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 coordination 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 forming of teams may promote overall activity, 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 field partner reliability. The paper presents a model for team recommendation to lenders who have no affiliation with a team.
To measure the performance, 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 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 single night freiburg 2014 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 be 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 single night freiburg 2014 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, single night freiburg 2014, 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, 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 single night freiburg 2014 delinquency rate, single night freiburg 2014, 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, the data set has the relations between lenders and teams, single night freiburg 2014, 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.
I know that promoting team creation improves activity, leading to more funding with single night freiburg 2014 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, single night freiburg 2014, 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 single night freiburg 2014 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, single night freiburg 2014, making up a total of On average, a team single night freiburg 2014 32 members, with a standard deviation of and a median of 4 members. Single night freiburg 2014 2 shows team logarithmic distribution by member count, single night freiburg 2014.
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?
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