12,623 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
Counterfactual Learning to Rank (LTR) methods optimize ranking systems using
logged user interactions that contain interaction biases. Existing methods are
only unbiased if users are presented with all relevant items in every ranking.
There is currently no existing counterfactual unbiased LTR method for top-k
rankings. We introduce a novel policy-aware counterfactual estimator for LTR
metrics that can account for the effect of a stochastic logging policy. We
prove that the policy-aware estimator is unbiased if every relevant item has a
non-zero probability to appear in the top-k ranking. Our experimental results
show that the performance of our estimator is not affected by the size of k:
for any k, the policy-aware estimator reaches the same retrieval performance
while learning from top-k feedback as when learning from feedback on the full
ranking. Lastly, we introduce novel extensions of traditional LTR methods to
perform counterfactual LTR and to optimize top-k metrics. Together, our
contributions introduce the first policy-aware unbiased LTR approach that
learns from top-k feedback and optimizes top-k metrics. As a result,
counterfactual LTR is now applicable to the very prevalent top-k ranking
setting in search and recommendation.Comment: SIGIR 2020 full conference pape
Finding Influential Users in Social Media Using Association Rule Learning
Influential users play an important role in online social networks since
users tend to have an impact on one other. Therefore, the proposed work
analyzes users and their behavior in order to identify influential users and
predict user participation. Normally, the success of a social media site is
dependent on the activity level of the participating users. For both online
social networking sites and individual users, it is of interest to find out if
a topic will be interesting or not. In this article, we propose association
learning to detect relationships between users. In order to verify the
findings, several experiments were executed based on social network analysis,
in which the most influential users identified from association rule learning
were compared to the results from Degree Centrality and Page Rank Centrality.
The results clearly indicate that it is possible to identify the most
influential users using association rule learning. In addition, the results
also indicate a lower execution time compared to state-of-the-art methods
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