TITLE:
A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria
AUTHORS:
Agaji Iorshase, Onyeke Idoko Charles
KEYWORDS:
Preference, Rating, Filtering, Serendipity, Ramp-Up, Cold-Start, Skip Gram
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.11,
November
19,
2015
ABSTRACT: Benue State of Nigeria is tagged the Food
Basket of the country due to its heavy production of many classes of food.
Situated in the North Central Geo-Political area of the country, its food
production ranges from root crops, fruits to cereals. Recommender systems (RSs)
allow users to access products of interest, given a plethora of interest on the
Internet. Recommendation techniques are content-based and collaborative
filtering. Recommender systems based on collaborative filtering outshines
content-based systems in the quality of their recommendations, but suffers from
the cold start problem, i.e., not being able to recommend items that have few
or no ratings. On the other hand, content-based recommender systems are able to
recommend both old and new items but with low recommendation quality in
relation to the user’s preference. This work combines collaborative filtering
and content based recommendation into one system and presents experimental
results obtained from a web and mobile application used in the simulation. The
work solves the problem of serendipity associated with content based (RS) as
well as the problem of ramp-up associated with collaborative filtering. The
results indicate that the quality of recommendation is promising and is
competitive with collaborative technique recommending items that have been seen
before and also effective at recommending cold-start products.