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.

Highlights

  • Recommender systems (RSs) are systems that filter out information

  • The Userprofile table was initialized with 23 hypothetical users who have rated 19 products in Benue state held on the Product table

  • The rating values for the products by the user are on a scale of 1 to 5, with 1 to 3 for poor rating and 4 and 5 for good ratings. 4.1

Read more

Summary

Introduction

Recommender systems (RSs) are systems that filter out information. They serve as decision support tools. They provide product and service recommendations tailored to the user’s needs and preferences. Recommender systems are intelligent personalized applications that suggest products or services, or more generally speaking information “items”, which best suit the user’s needs and preferences, in a given situation and context [1] [2]. Ramp-up arises either because there are no enough rank ratios for a new user or there is no enough ranking on an item To address these problems, it is desirable to combine the RS techniques to leverage on the advantage(s) provided by individual techniques in order to improve recommendation accuracy; the need for a hybrid approach which is the basis of this work

Review of Related Literature
Methodology
Results and Discussions
Experiment 3
Conclusion and Recommendation
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call