A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System
Cognitive similarity (CgS); collaborative filtering (CF); cosine measure; Goweras coefficient; Jaccard coefficient; recommendation system (RS); sigmoid function; similarity measure
Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user's ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers-Jaccard-Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that. 2014 IEEE.
Jain G.; Mahara T.; Sharma S.C.; Sangaiah A.K.
IEEE Transactions on Computational Social Systems, Vol-9, No. 6, pp. 1785-1793.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/TCSS.2022.3187430 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134217229&doi=10.1109%2fTCSS.2022.3187430&partnerID=40&md5=5f34db2778fa985e5d20a8285a9b1576
Jain G., IIT Roorkee, Electronic and Computer Discipline, Department of Polymer and Process Engineering, Roorkee, 247667, India; Mahara T., IIT Roorkee, Department of Polymer and Process Engineering, Roorkee, 247667, India, Christ University, School of Business and Management, Bengaluru, 560029, India; Sharma S.C., IIT Roorkee, Electronic and Computer Discipline, Department of Paper Technology, Roorkee, 247667, India; Sangaiah A.K., National Yunlin University of Science and Technology, Department of Industrial Engineering and Management, Douliu, 64002, Taiwan