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Joint Topic-Semantic-aware Social Recommendation for Online Voting
Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge
Management (CIKM 2017
From Group Recommendations to Group Formation
There has been significant recent interest in the area of group
recommendations, where, given groups of users of a recommender system, one
wants to recommend top-k items to a group that maximize the satisfaction of the
group members, according to a chosen semantics of group satisfaction. Examples
semantics of satisfaction of a recommended itemset to a group include the
so-called least misery (LM) and aggregate voting (AV). We consider the
complementary problem of how to form groups such that the users in the formed
groups are most satisfied with the suggested top-k recommendations. We assume
that the recommendations will be generated according to one of the two group
recommendation semantics - LM or AV. Rather than assuming groups are given, or
rely on ad hoc group formation dynamics, our framework allows a strategic
approach for forming groups of users in order to maximize satisfaction. We show
that the problem is NP-hard to solve optimally under both semantics.
Furthermore, we develop two efficient algorithms for group formation under LM
and show that they achieve bounded absolute error. We develop efficient
heuristic algorithms for group formation under AV. We validate our results and
demonstrate the scalability and effectiveness of our group formation algorithms
on two large real data sets.Comment: 14 pages, 22 figure
Group Modeling : selecting a sequence of television items to suit a group of viewers
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