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 roo...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.展开更多
文摘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.
