1 research outputs found
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
Recently, the reciprocal recommendation, especially for online dating
applications, has attracted more and more research attention. Different from
conventional recommendation problems, the reciprocal recommendation aims to
simultaneously best match users' mutual preferences. Intuitively, the mutual
preferences might be affected by a few key attributes that users like or
dislike. Meanwhile, the interactions between users' attributes and their key
attributes are also important for key attributes selection. Motivated by these
observations, in this paper we propose a novel reinforced random convolutional
network (RRCN) approach for the reciprocal recommendation task. In particular,
we technically propose a novel random CNN component that can randomly convolute
non-adjacent features to capture their interaction information and learn
feature embeddings of key attributes to make the final recommendation.
Moreover, we design a reinforcement learning based strategy to integrate with
the random CNN component to select salient attributes to form the candidate set
of key attributes. We evaluate the proposed RRCN against a number of both
baselines and the state-of-the-art approaches on two real-world datasets, and
the promising results have demonstrated the superiority of RRCN against the
compared approaches in terms of a number of evaluation criteria.Comment: 10 pages, 5 figure