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The Graph-based Broad Behavior-Aware Recommendation System for Interactive News
In this paper, we propose a heuristic recommendation system for interactive
news, called the graph-based broad behavior-aware network (G-BBAN). Different
from most of existing work, our network considers six behaviors that may
potentially be conducted by users, including unclick, click, like, follow,
comment, and share. Further, we introduce the core and coritivity concept from
graph theory into the system to measure the concentration degree of interests
of each user, which we show can help to improve the performance even further if
it's considered. There are three critical steps in our recommendation system.
First, we build a structured user-dependent interaction behavior graph for
multi-level and multi-category data as a preprocessing step. This graph
constructs the data sources and knowledge information which will be used in
G-BBAN through representation learning. Second, for each user node on the
graph, we calculate its core and coritivity and then add the pair as a new
feature associated to this user. According to the definition of core and
coritivity, this user-dependent feature provides useful insights into the
concentration degree of his/her interests and affects the trade-off between
accuracy and diversity of the personalized recommendation. Last, we represent
item (news) information by entity semantics and environment semantics; design a
multi-channel convolutional neural network called G-CNN to learn the semantic
information and an attention-based LSTM to learn user's behavior
representation; combine with previous concentration feature and input into
another two fully connected layers to finish the classification task. The whole
network consists of the final G-BBAN. Through comparing with baselines and
several variates of itself, our proposed method shows the superior performance
in extensive experiments.Comment: 13 page