Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Collaborative Approach to Angel and Venture Capital Investment
  Recommendations   Matrix factorization was used to generate investment recommendations for
investors. An iterative conjugate gradient method was used to optimize the
regularized squared-error loss function. The number of latent factors, number
of iterations, and regularization values were explored. Overfitting can be
addressed by either early stopping or regularization parameter tuning. The
model achieved the highest average prediction accuracy of 13.3%. With a similar
model, the same dataset was used to generate investor recommendations for
companies undergoing fundraising, which achieved highest prediction accuracy of
11.1%.

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The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th