Abstract
Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.
Highlights
Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item
Recommender systems are commonly used to address the problem of information overload by recommending most suitable items that meets the interests of the user
Recommender systems are broadly categorized into collaborative filtering based [1] and content based approaches [2]
Summary
Rana et al [5] proposed a recommender system to provide recommendations about relevant books to the users by finding other users having same preferences This system [5] introduces a concept of temporal dimension which computes the frequency of user liking of any item in a specific time period. To address the above mentioned limitations of content based and collaborative filtering recommendation approaches we have proposed a hybrid books recommendation approach for mobile platforms. The proposed hybrid scheme is developed for mobile platform that provides most relevant and latest books recommendations to the users based on their preference, history, and neighborhood users. The proposed hybrid scheme uses a simple approach to provide better efficiency to the end users as compared to classifier based approach where the recommendation accuracy can be improved but at the expense of increased computational cost.
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