Background:
Recommender Systems use user interests to provide more accurate recommendations
according to user actual interests and behavior.
Methods:
This work aims at improving recommender systems by discovering hidden user interests
from the existing interests. User interest expansion would contribute in improving the accuracy of
recommender systems by finding more user interests using the given ones. Two methods are proposed
to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding
interests using word embeddings.
Results:
Experimental work shows that such expanding is efficient in terms of accuracy and execution
time.
Conclusion:
Therefore, expanding user interests proved to be a promising step in the improvement
of the recommender systems performance.
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