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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 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 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, loan count, loan amount, member count, membership type open or closed , date 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. 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. 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 . 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 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 in average fund loans, while open teams invest in , with 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, 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. Figure 3 shows the distribution of loans funded by each membership type. The distribution is similar, but the right tail of the open membership is longer, meaning more loans get funded by this type of teams. Kiva should promote the creation of open teams. Some additional experiments performed on the Kiva data are shown in the following section.
It includes clustering, dimensionality reduction and filtering. To support the main goal of producing more activity in the Kiva ecosystem by forming teams, we need to find a source of reasons from which teams my be created, hereby identifying similar lenders. One direct way of doing that is to investigate the loans and try to determine clusters of loans from which we may create a reason to lend. Imagine that we identify clusters in which a certain sector is relevant, or perhaps a combination of attributes such as country and sector.
We could theoretically create clusters, and identify which reason to loan they would satisfy. From here, we would have to identify which teams or lenders match with these clusters in order to make a recommendation. To cluster the loans, I have implemented a Kmeans algorithm enhanced with principal component analysis.
Clusters are created using the loan purpose that was given by the borrower. This is an open text attribute where the borrower defines the use of the money. As I was preprocessing, I have created vectors of words representing each loan. Since we know that each loan is categorized into sectors, I set the number of cluster to 12, hoping to see a relation.
However, the results show good cluster, but no relation to the sector. As we can see in figure 4 below, the clusters created using the algorithm are not at all the same as the actual clusters created by the actual sector. The figure shows clusters of loans created by the algorithm in the first column. The second column shows the loans colored by sector. In the top left graph we can see that the two main principal components cluster the loans in a good way. This effect is not aligned to the sector of each loan.
With the same idea of identifying clusters, I reproduced the experiment over teams. The attributed reason to loan is stated by the teams, which is also an open text field. Unfortunately the clusters created for teams are not as good. Teams may be seen as filters, if there is one for fishing, having two or more is unlikely. Arranging teams together in clusters is a harder task.
Figure 5 shows the teams clusters. The following figures show some of the centroids of the clusters that showed a clear objective. As mentioned before, the languages in the dataset vary, with English being the most common language, followed by Spanish. Out of the twelve clusters, 7 of them are English and 5 are in Spanish. The complete set of images can be seen in the appendix.
Figure 6 shows Cluster number 9, with the most relevant words for the cluster being clothing related items. It has the type of cloth for women, men and children the type of article, such as pants, shirts, shoes, blouses, and so on. In addition, the cluster has some food related words like lard, rice, milk and other ingredients.
This cluster would suggest creating teams and finding lenders that are interested in providing clothing and increase food inventory for business owners. Another interesting cluster is number 12, which refers to housing. It also includes some business related terms for real estate. From this cluster we can identify users that may want to fund infrastructure, housing and real estate projects. One of the Spanish related clusters is shown in Figure 8. This cluster is all about food, and since Kiva finances projects, we can think that these clusters aim to building inventories, creating a convenience store or even setting up a restaurant.
This approach could yield good results if we are able to identify a reason to fund within teams or lenders that corresponds to these clusters. For future work, it would be helpful to optimize this approach in order to create more possible purposes and identify teams and lenders that have funded similar loans in the past.
Since both lenders and teams provide open statements about their own motivation to lend, based on related work previously mentioned, this suggests that teams increase activity in the ecosystem. I decided to review similarities between the two, as to be able to enhance or create recommendations.
If it is easy for a lender to explore teams, and is reasonable to think that they would join the one that is most similar to them, we should expect to see a natural match between lenders in a team. All participants are encouraged to post everything in English, so that exposure to lenders is greater and becomes more effective. However, not every team and every lender states their purpose in a single language.
The data set has most of the information in English, but it also has Spanish, French, and Portuguese, amongst other languages. There may be a problem translating each text to English in case expressions are being used that can not be translated directly. For that reason I decided to run the analysis using original languages. To measure the similarities I implemented tf. The approach to follow is the same as used search engines use. In first place a dictionary is built, based on the collection of documents to be indexed.
When a new document is added, the dictionary is updated. When a query is executed, the dictionary is used to determine which document is most similar to it. I do not need to update the dictionary, since there are no new teams and I am matching lenders to teams based on their reason to loan. Finally, the query would put forward the reason for each lender to lend.
To measure the similarities, I have used cosine similarity weighted by tf. Since the overall goal of this paper is to provide a team recommendation, a subset is created selecting lenders that meet the following constraints: a having provided a reason to loan and b are members of more than one team.
When applying these criteria, a total of 7, lenders are selected. The similarity of each lender was measured against each of the 11, teams, recording a vector with the most similar teams for each user. If we predict only one team for a given lender, only 1.
Another method to evaluate this approach is to produce as many recommendations as possible from the list of the most similar teams, by setting a similarity threshold. When doing this, overall accuracy can be improved significantly to 6. Figure 9 shows the accuracy at different thresholds.
This means that lenders do not select their teams based primarily on their reason to loan. There is no easy way for a lender to find his most similar team based on his or her reason to loan via the Kiva website. The only recommendations that are given are based on what other people search. However, the team data analyzed in this paper is 6 years older and ten times the size of that what was reviewed by the authors. Either selecting a team is coherent among similar users but has no significant relation to team goal, or lender behavior shifts in time.
Choo, J. It has become the preferred and predominant measure to determine the cost of real estate, values of housing and any secured loan, either private or of the Chilean government. Individual payments are made in Chilean pesos the country's legal tender , according to the daily value of the UF. A similar currency unit for use generally in payment of taxes, fines, or customs duty is the Unidad Tributaria Mensual UTM literally: monthly tax unit.
From its creation in , each calendar quarter, the UF value of Chilean escudo would be quoted based on the Consumer Price Index CPI of the past three months, which would be the official rate for the following quarter. In October with the currency changeover to pesos, the value of 1 UF was quoted in pesos and readjusted monthly.
In July it was calculated daily by interpolation between the 10th of each month and the 9th of the following month, according to the monthly variation of the CPI. Since the Central Bank of Chile has determined its value. Historical values of the UF as of 31 December each year unlike below, in Chile numbers are written with a decimal comma and a dot to separate thousands :.
From Wikipedia, the free encyclopedia. UF's calculation over time [ edit ] From its creation in , each calendar quarter, the UF value of Chilean escudo would be quoted based on the Consumer Price Index CPI of the past three months, which would be the official rate for the following quarter.
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