Shahab Saquib Sohail

Book recommendation system using opinion mining technique

With the changing trends in technologies, and brisk growth in Internet, daily life of an individual has also changed at a very fast pace. The impacts of these technologies are so diverse that it has affected almost every sphere of the life. People start using applications of the Internet in their daily life; they prefer online shopping for their needs more and more. For academician, researchers and students, purchasing the desired book from huge collections of books on the Internet is very tedious work. In this paper, we presented a recommendation technique based on opinion mining to propose top ranked books on different discipline of the computer science. Based on the need of the customers and the reviews collected from them, we have categorized features for the books. We analyze the features on the basis of several characteristics that we have categorized and reviews of the users. Weights are assigned to categorized features according to their importance and usage, and accordingly the ranks are given. Finally, top ten ranked books are listed. This method is expected to be helpful for millions of the users who seek for desired books.

OWA based Book Recommendation Technique

Abstract The proliferation of the modern technologies has caused data overload over the Internet. The huge data over the World Wide Web has increased the problems for the users to extract the exact information. Variousrecommendation techniques are being used to help the customers in purchasing the desired items for online shopping. In this paper, we propose a recommendation method for books. We use positional aggregation based scoring (PAS) technique to score the books recommended by top ranked universities and assigned weights to these scores using fuzzy quantifiers. We apply Ordered Weighted Averaging (OWA) aggregation operator over these scores to find the top books. Finally top ranked books are recommended.

User Feedback Based Evaluation of a Product Recommendation System Using Rank Aggregation Method

The proliferation of the Internet has changed the daily life of a common man. There is a diverse effect of rapid growth of Internet in the daily life. The influence of Internet has changed the way we live and even the way we think. The use of the Internet for purchasing different products of the daily needs has increased exponentially in recent years. Now customers prefer online shopping for the acquisition of the various products. But the huge e-business portals and increasing online shopping sites make it difficult for the customers to go for a particular product. It is very common practice that a customer wishes to know the opinion of other consumers who already have acquired the same product. Therefore we tried to involve the human judgment in recommending the products to the users using implicit user feedback and applied a rank aggregation algorithm on these recommendations. In this paper we chose few products and their respective ranks arbitrarily taken from previous work. For obtaining user’s purchase activities a vector feedback is taken from the user and on the basis of their feedback, products are scored; hence they are again ranked which gives each user’s ranking. We propose a rank aggregation algorithm and apply it on individuals ranking to get an aggregated final users’ ranking. Finally we evaluate the system performance using false negative rates, false positive rates, and precision. These measures show the effectiveness of the proposed method.

User Feedback Scoring and Evaluation of a Product Recommendation System

With the legerity of the Internet, daily life of a common man has changed. Rapid growth of the Internet has a diverse effect on the daily life. The influence of the Internet has changed the way we live and even the way we think. The use of Internet for purchasing different products of the daily needs has increased exponentially in recent years. Now customers prefer online shopping for the acquisition of the various products. But the huge e-business portals and increasing online shopping sites make it difficult for the customers to go for a particular product. It is very common practice that a customer wishes to know the perception of other consumers who already have acquired the same product. Therefore we tried to involve the human judgment in recommending the products to the users using implicit user feedback. In this paper we chose few products and their respective ranks arbitrarily taken from previous work. For obtaining users purchase activities a vector feedback is taken from the user and on the basis of their feedback products are scored, hence they are again ranked which is supposed to be the users ranking. Finally we evaluate the system performance using false negative and false positive rates, which show the effectiveness of the proposed method.

An OWA-Based Ranking Approach for University Books Recommendation: OWA-BASED RANKING APPROACH FOR BOOK RECOMMENDATION

Generally the book recommendation approaches are personalized in nature, that is, they utilize the users’ purchasing behavior to recommend them the book similar to their preferences. The main problem with the personalized recommendation is its knowledge requirement about users’ past preferences. As a result, these techniques fail in producing appropriate recommendation for a new user whose preferences are not known. The personalized recommendation also needs extra space to store the users’ preferences. In this paper, a framework to recommend books to university students for their studies is presented. In order to answer which books are to be included in the syllabus, a specialized way of recommendation, where recommendations from experts of the subjects at different universities are considered, is presented. We have suggested a ranked recommendation approach for books, which employ Ordered Weighted Aggregation (OWA), a fuzzy‐based aggregation, to aggregate the several ranking of the top universities. On the one hand, it does not need user prior preferences, and on the other hand, it eases the complexities of personalized recommendation to huge number of users and replaces it with a single ranked recommendation. The experimental results are compared with the existing positional aggregation algorithm that demonstrates significant improvement in the results with respect to various performance metrics.

Book Recommender System using Fuzzy Linguistic Quantifier and Opinion Mining

The recommender systems are being used immensely to promote various services, products and facilities of daily life. Due to the success of this technology, the reliance of people on the recommendations of others is increasing with tremendous pace. One of the best and easiest ways to acquire the suggestions of the other like-minded and neighbor customers is to mine their opinions about the products and services. In this paper, we present a feature based opinion extraction and analysis from customers’ online reviews for books. Ordered Weighted Aggregation (OWA), a well-known fuzzy averaging operator, is used to quantify the scores of the features. The linguistic quantifiers are applied over extracted features to ensure that the recommended books have the maximum coverage of these features. The results of the three linguistic quantifiers, ‘at least half’, ‘most’ and ‘as many as possible’ are compared based on the evaluation metric - precision@5. It is evident from the results that quantifier ‘as many as possible’ outperformed others in the aforementioned performance metric. The proposed approach will surely open a new chapter in designing the recommender systems to address the expectation of the users and their need of finding relevant books in a better way.

UMW: A model for enhancement in wearable technology based on opinion mining technique

The modern tools and techniques have given a great opportunities to the researchers to involve these advancement in solving the daily life issues, and making an easy platform to fulfill the need of a common man. In recent days a great revolution in a common mans daily life has been observed by the use of wearable technology based devices, i.e. wearable devices. These devices get a great and positive response from the perspective of business markets which lead the researchers to incline towards this field of research. In this paper, we have discussed several wearable devices and studied a framework, which is based on opinion mining to enhance the wearable technologies. The enhancement may support manufacturers to produce an efficient wearable device for the consumers. The proposed model, “User feedback based Model for Enhancement of Wearable Technology” (UWM) in the concerned study presented a two-step procedure for enhancement. We have concluded by our study that the proposed model, UMW, may help in enhancing the quality of the wearable devices.

Book Recommender System Using Fuzzy Linguistic Quantifiers

The recommender systems are used to facilitate the users with appropriate choices according to their preferences for various online services. Due to the increasing need, various recommendation systems have been developed including recommendation for music, book, movie, etc. The book recommendation technique usually explores the rating of the users for the particular product to recommend it to other users. Instead of utilizing users’ reviews, we have proposed an authorities recommendation approach which exploits ranking of the books by different top-ranked universities. These rankings are aggregated using OWA. Ordered Weighted Aggregation (OWA), a well-known fuzzy averaging operator, is used to aggregate different rankings of the books given by respective universities. The rank of the books is converted into scores using Positional Aggregation based Scoring (PAS) technique. The linguistic quantifiers are applied over these scores and the value of three linguistic quantifiers, ‘at least half’, ‘most’ and ‘as many as possible’, are compared with amazon ranking, evaluated on the basis of ranks explicitly taken from experts. P@10, FPR@10 and Mean Average Precision (MAP) are evaluated. It is evident from the results that quantifier ‘at least half’ outperformed others in the aforementioned performance metric. It is envisaged that the proposed approach will help the research community in designing the recommender systems to explore the relevant books and meet the expectation of the users in a better way.