Abstract
In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.
Highlights
The roots of recommender systems can be traced back to the extensive work in information retrieval, approximation theory, cognitive science, forecasting theories, consumer choice modeling in marketing, as well as evidences and links to management sciences [16, 7, 2, 27, 13]
In the mid 1990’s, when recommendation problems that unambiguously rely on ratings structure started attracting researchers with diverse approaches, recommender systems surfaced as an independent research area
Nearest User Rated Material (NUR): the weights of the current user are affected by each cold-start question
Summary
The roots of recommender systems can be traced back to the extensive work in information retrieval, approximation theory, cognitive science, forecasting theories, consumer choice modeling in marketing, as well as evidences and links to management sciences [16, 7, 2, 27, 13]. Hybrid-based recommender systems continue to gain more and more attention from researchers to overcome recommendation issues like over-specialization, content description, sparsity, early-rater problem, gray-ship, subjective domain and, most sinister, cold-start problems [14, 17]. Papers are considered relevant that are published by the same author or journal, which at times is inaccurate Another hybrid-based paper recommender system was released as the output of the research work by Pandya et al, so as to increase the quality of recommendations [27]. Various researchers continue to display remarkable effort towards devising hybrid algorithms for recommendations of various products, many at times failed to produce models that will combine multiple techniques to overcome multiple drawbacks associated with such e-learning recommendations. These research where generally to improve the users and items understanding, incorporate the contextual information into the recommendation process, supports multi-criteria ratings and provide more flexible and less intrusive types of recommendations [2, 10, 30, 31, 32]
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