Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Jalal, Ahmed Adeeb
Affiliations: Computer Engineering Department, College of Engineering, AL-Iraqia University, Baghdad, Iraq | E-mail: [email protected]
Correspondence: [*] Corresponding author: Computer Engineering Department, College of Engineering, AL-Iraqia University, Baghdad, Iraq. E-mail: [email protected].
Abstract: Web growth, especially in social networks, is continuously increasing every day. Multiplicity of products offered and web pages has made picking up relevant items a tedious job. On the other hand, different tastes and behaviors of users is creating the probability to find a similar user among a large group of users difficult. As a result, automated software systems have difficulty to discover what is interesting to users. We have proposed a new approach to adapt to this flow. We will exploit domain knowledge of training data set to create a summary matrix. The summary matrix consists of new and few columns according to the attribute values of the selected feature. We fill the summary matrix with the average ratings based on the number of times that the attribute values appear in the user’s profile for rated items. We use the summary matrix in two hybrid recommender systems. In our approach, we use meta-level technique which is one of the pipelined hybridization techniques. The proposed approach will reduce the effects of sparsity, cold start, and scalability which are common problems with the collaborative recommender systems. Furthermore, the proposed approach will improve the recommendation accuracy when there is comparison with the Collaborative Filtering Pearson Correlation approach and it will be faster as well.
Keywords: Big data, recommender systems, feature engineering, hybrid recommender systems, meta-level, Collaborative Filtering, Content-Based Filtering, sparsity, cold start, scalability
DOI: 10.3233/KES-180383
Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 22, no. 3, pp. 177-193, 2018
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]