Incorporating Reverse Search for Friend Recommendation
with Random Walk
Qing
Yang1, Haiyang Wang1,
Mengyang Bian1, Yuming Lin2, and
Jingwei Zhang2
1Guangxi Key Laboratory of Automatic
Measurement Technology and Instrument, Guilin University of Electronic Technology,
China
2Guangxi Key Laboratory of Trusted Software,
Guilin University of Electronic Technology, China
Abstract: Recommending friends is an important mechanism for
social networks to enhance their vitality and attractions to users. The huge
user base as well as the sparse user relationships give great challenges to
propose friends on social networks. Random walk is a classic strategy for
recommendations, which provides a feasible solution for the above challenges.
However, most of the existing recommendation methods based on random walk are
only weighing the forward search, which ignore the significance of reverse
social relationships. In this paper, we proposed a method to recommend friends by
integrating reverse search into random walk. First, we introduced the FP-Growth
algorithm to construct both web graphs of social networks and their
corresponding transition probability matrix. Second, we defined the reverse
search strategy to include the reverse social influences and to collaborate
with random walk for recommending friends. The proposed model both optimized
the transition probability matrix and improved the search mode to provide
better recommendation performance. Experimental results on real datasets showed
that the proposed method performs better than the naive random walk method
which considered the forward search mode only.
Keywords: Social networks, friend recommendation,
reverse search.
Received September 2, 2017; accepted April 25, 2018
https://doi.org/10.34028/iajit/17/3/2