User-Oriented Preference Toward a Recommender System

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Pei-Chun Lin
Nureize Arbaiy

Abstract

            Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides information related to movies, television programs, home videos, video games, and streaming content that also collects many ratings and reviews from users. This paper also analyzed individual user data to extract the user’s features. Based on user characteristics, movie ratings/scores, and movie results, a UOP-RS model was built. In our experiment, 5000 IMDb movie datasets were used and 5 recommended movies for users. The results show that the system could return results on 3.86 s and has a 14% error on recommended goods when training data as . At the end of this paper concluded that the system could quickly recommend users of the goods which they needed.  The proposed system will extend to connect with the Chatbot system that users can make queries faster and easier from their phones in the future.

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1.
Lin P-C, Arbaiy N. User-Oriented Preference Toward a Recommender System. Baghdad Sci.J [Internet]. 2021Mar.30 [cited 2021Apr.13];18(1(Suppl.):0746. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5929
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References

Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. In Recommender systems handbook 2011 (pp. 1-35). Springer, Boston, MA.

Pal A, Parhi P, Aggarwal M. An improved content-based collaborative filtering algorithm for movie recommendations. In2017 tenth international conference on contemporary computing (IC3) 2017 Aug 10 (pp. 1-3). IEEE.

Chen AY, McLeod D. Collaborative filtering for information recommendation systems. In Encyclopedia of E-Commerce, E-Government, and Mobile Commerce 2006 (pp. 118-123). IGI Global.

Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in artificial intelligence. 2009;2009.

Aiolli F. A Preliminary Study on a Recommender System for the Million Songs Dataset Challenge. In IIR 2013 Jan 16 (pp. 73-83).

Halder S, Sarkar AJ, Lee YK. Movie recommendation system based on movie swarm. In 2012 Second International Conference on Cloud and Green Computing 2012 Nov 1 (pp. 804-809). IEEE.

Kbaier ME, Masri H, Krichen S. A personalized hybrid tourism recommender system. In2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) 2017 Oct 1 (pp. 244-250). IEEE.

Bobadilla J, Bojorque R, Esteban AH, Hurtado R. Recommender systems clustering using Bayesian nonnegative matrix factorization. IEEE Access. 2017 Dec 29;6:3549-64.

Neamah AA, El-Ameer AS. Design and Evaluation of a Course Recommender System Using Content-Based Approach. In 2018 International Conference on Advanced Science and Engineering (ICOASE) 2018 Oct 9 (pp. 1-6). IEEE.

Baltrunas L, Ludwig B, Ricci F. Matrix factorization techniques for context-aware recommendation. InProceedings of the fifth ACM conference on Recommender systems 2011 Oct 23 (pp. 301-304).

Zhang R, Mao Y. Movie Recommendation via Markovian Factorization of Matrix Processes. IEEE Access. 2019 Jan 11;7:13189-9

Walek B, Spackova P. Content-based recommender system for online stores using expert system. In 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) 2018 Sep 26 (pp. 164-165). IEEE.

Danil B, Elena Y, Ekaterina P. Similarity Measures and Models for Movie Series In Recommender System. International Conference on Internet Science 2018 Oct 24 (pp. 181-193). Springer, Cham.

Li J, Xu W, Wan W, Sun J. Movie recommendation based on bridging movie feature and user interest. J Comput Sci. 2018 May 1;26:128-34.

Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods. In Recommender systems handbook 2011 (pp. 107-144). Springer, Boston, MA.

Lin PC, Arbaiy N. A Novel Classifier for a Kansei Recommender System. In2018 IEEE International Conference on Cognitive Computing (ICCC) 2018 Jul 2 (pp. 114-117). IEEE.

Lin PC, Arbaiy N. An Algorithm Design of Kansei Recommender System. InInternational Conference on Soft Computing and Data Mining 2018 Feb 6 (pp. 115-123). Springer, Cham.