Monday, August 11, 2014

PAPER: A Novel C2C E-Commerce Recommender System

A Novel C2C E-Commerce Recommender System Based on Link Prediction: Applying Social Network Analysis    PDF only

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.

Please read:
Some new and fresh PAPERS from The 37th Annual ACM SIGIR 2014 CONFERENCE

Please remember:
Personality Based Recommender Systems are the next generation of recommender systems because they perform far better than Behavioural ones (past actions and pattern of personal preferences)

What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.

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