Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel C2C E-Commerce Recommender System Based on Link Prediction:
  Applying Social Network Analysis   Social network analysis emerged as an important research topic in sociology
decades ago, and it has also attracted scientists from various fields of study
like psychology, anthropology, geography and economics. In recent years, a
significant number of researches has been conducted on using social network
analysis to design e-commerce recommender systems. Most of the current
recommender systems are designed for B2C e-commerce websites. This paper
focuses on building a recommendation algorithm for C2C e-commerce business
model by considering special features of C2C e-commerce websites. In this
paper, we consider users and their transactions as a network; by this mapping,
link prediction technique which is an important task in social network analysis
could be used to build the recommender system. The proposed tow-level
recommendation algorithm, rather than topology of the network, uses nodes
features like: category of items, ratings of users, and reputation of sellers.
The results show that the proposed model can be used to predict a portion of
future trades between users in a C2C commercial network.

29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas