A Context-Aware Personalized Hybrid Book Recommender System

Keywords: Recommendation system, context – aware, personality, demographic, location, review sentiment, purchase reason


Contextual information such as emotion, location and time can effectively improve product or service recommendations, however, studies incorporating them are lacking. This paper presents a context-aware recommender system, personalized based on several user characteristics and product features. The recommender system which was customized to recommend books, was aptly named as a Context-Aware Personalized Hybrid Book Recommender System, which utilized users’ personality traits, demographic details, location, review sentiments and purchase reasons to generate personalized recommendations. Users’ personality traits were determined using the Ten Item Personality Inventory. The results show an improved recommendation accuracy compared to the existing algorithms, and thus indicating that the integration of several filtering techniques along with specific contextual information greatly improves recommendations.


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Author Biographies

Hossein Arabi, Bookurve Sdn. Bhd., Malaysia

Hossein Arabi received his Ph.D in information system from University of Malaya in 2018. He is currently working as a solution architect which is leading the design, development, implementation and testing of IT solutions. Over the years, he has been involved in development of variety systems, from monolithic architecture to modular-based and now on micro-service architecture.

Vimala Balakrishnan, Department of Information Systems, University of Malaya, Kuala Lumpur 50603, Malaysia

Vimala Balakrishnan is an Associate Professor and a Fulbright Research Scholar affiliated with the Faculty of Computer Science and Information Technology, University of Malaya since 2010. She obtained her PhD. in the field of Ergonomics from Multimedia University, whereas her Masters and Bachelor degrees were from University of Science Malaysia. Dr Balakrishnan’s main research interests are in data analytics and sentiment analysis, particularly related to social media. Her research domains include healthcare, education and social issues such as cyberbullying. She has published approximately 60 articles in top indexed journals and 44 conference proceedings, has four patents and eight copy-rights.

Nor Liyana Mohd Shuib, Department of Information Systems, University of Malaya, Kuala Lumpur 50603, Malaysia

Nor Liyana Mohd Shuib obtained her Master of Information System (Data Mining) from Universiti Kebangsaan Malaysia in 2005 and a Ph.D. from the University of Malaya, Malaysia in 2013 respectively. She is a Senior Lecturer at the Department of Information Systems, Faculty of Computer Science & Information Technology, University of Malaya, Malaysia. She has published a number of journal papers and proceedings locally and internationally. Her research interests include personalization, e-learning, recommender system, data science, data mining, artificial intelligence application, and educational technology. She has won more than 20 awards from reputable innovation competition internationally. She is also a Senior Member of IEEE computing society, an active blogger and presently, the principal investigator of multiple research grant in the Faculty.


Bhosale, S., Nimse, P., Wadgaonkar, S., & Yeole, A. (2017). SuggestA-Book: A Book Recommender Engine with Personality based Mapping. International Journal of Computer Applications, 159(9), 1–4.

Kim, E., Kim, S.-Y., Kim, G.-A., Kim, M., Rho, S., Man, K. L., & Chong, W. K. (2015). TiMers: Time-based Music Recommendation System based on Social Network Services Analysis. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).

Winoto, P., & Tang, T. Y. (2010). The role of user mood in movie recommendations. Expert Systems with Applications, 37(8), 6086–6092.

Braunhofer, M., Elahi, M., & Ricci, F. (2014). Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System, In: Hepp M., Hoffner Y. (eds.) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, 188. Springer, Cham.

Cantador, I., Fernández-tobías, I., & Bellogín, A. (2013). Relating Personality Types with User Preferences in Multiple Entertainment Domains. Proceedings of the 1st Workshop on Emotions and Personality in Personalized Services (EMPIRE), (pp. 1–16).

Chen, Q., Zheng, S., Chen, H., & Liu, W. (2017). Research on Recommendation Mode of WeChat Official Account Platform Based on Hybrid Recommendation Algorithm. DEStech Transactions on Environment, Energy and Earth Sciences, 57–61.

Nirwan, H., Verma, O. P., & Kanojia, A. (2016). Personalized hybrid book recommender system using neural network. In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 1281–1288).

Zhang, Y., Chen, M., Huang, D., Wu, D., & Li, Y. (2017). iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems, 66, 30–35.

Arabi, H., & Balakrishnan, V. (2019). Personalized Hybrid Book Rec-ommender. International Journal of Information Systems in the Service Sector, 11 (3), 70–97.

Lu, C.-C., & Tseng, V. S. (2009). A novel method for personalized music recommendation. Expert Systems with Applications, 36(6), 10035–10044.

McCrae, R. R., & Costa, P. T. (1996). Toward a New Generation of Personality Theories: Theoretical Contexts for the Five-Factor Model. [In] Wiggins JS (Ed.): The Five-Factor Model of Personality: Theoretical Perspectives. Guilford, New York.

Chiorri, C., Bracco, F., Piccinno, T., Modafferi, C., & Battini, V. (2015). Psychometric Properties of a Revised Version of the Ten item Personality Inventory. European Journal of Psychological Assessment, 31(2), 109–119.

Yang, B., Lei, Y., Liu, J., & Li, W. (2016). Social collaborative filtering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(8), 1633–1647.

Luo, C., Pang, W., Wang, Z., & Lin, C. (2014). Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations. In 2014 IEEE International Conference on Data Mining (pp. 917–922).

Guo, X., Feng, L., Liu, Y., & Han, X. (2016). Collaborative filtering model of book recommendation system. International Journal of Advanced Media and Communication, 6(2–4), 283–294.

Choi, J., Lee, H. J., & Kim, Y. C. (2011). The Influence of Social Presence on Customer Intention to Reuse Online Recommender Systems: The Roles of Personalization and Product Type, International Journal of Electronic Commerce, 16(1), 129–154.

Zhao, W. X., Li, S., He, Y., Wang, L., Wen, J.-R., & Li, X. (2016). Exploring demographic information in social media for product recommendation. Knowledge and Information Systems, 49(1), 61–89.

Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.

Chen, C.-C., & Tsai, J.-L. (2017). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems., 9, 628–638.

Bao, J., Zheng, Y., & Mokbel, M. F. (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 199–208).

Lu, Z., Dou, Z., Lian, J., Xie, X., & Yang, Q. (2015). Content-Based Collaborative Filtering for News Topic Recommendation. In AAAI (pp. 217–223).

Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book Recommendation System through content based and collaborative filtering method. In Data Mining and Advanced Computing (SAPIENCE), International Conference on (pp. 47–52).

Garrido, A. L., & Ilarri, S. (2014). TMR: a semantic recommender system using topic maps on the items’ descriptions. In European Semantic Web Conference (pp. 213–217).

Erekhinskaya, T., Balakrishna, M., Tatu, M., & Moldovan, D. (2016). Personalized Medical Reading Recommendation: Deep Semantic Approach. In International Conference on Database Systems for Advanced Applications (pp. 89–97).

Ratsameethammawong, P., & Kasemsan, K. (2010). Mobile Phone Location Tracking by the Combination of GPS, Wi-Fi and Cell Location Technology. Communications of the IBIMA. 2010, Article ID 566928.

Kumar, A., Gupta, S., Singh, S. K., & Shukla, K. K. (2015). Comparison of various metrics used in collaborative filtering for recommendation system. In Contemporary Computing (IC3), 2015 Eighth International Conference on (pp. 150–154).

Kelley, K. & Lai, K. (2011) Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation: Sample Size Planning for Narrow Confidence Intervals, Multivariate Behavioral Research, 46, 1–32.

Xin, L., Haihong, E., Junjie, T., Meina, S., & Yi, L. (2014). Enhancing Book Recommendation with Side Information. In Service Sciences (ICSS), 2014 International Conference on (pp. 142–146).

Guan, C., Guan, C., Qin, S., Qin, S., Ling, W., Ling, W., . . . Ding, G. (2016). Apparel recommendation system evolution: an empirical review. International Journal of Clothing Science and Technology, 28(6), 854–879.

Gil, J.-M., Lim, J., & Seo, D.-M. (2016). Design and Implementation of MapReduce-Based Book Recommendation System by Analysis of Large-Scale Book-Rental Data. In J. J. (Jong H. Park, H. Jin, Y.-S. Jeong, & M. K. Khan (Eds.), Advanced Multimedia and Ubiquitous Engineering: FutureTech {&} MUE (pp. 713–719). Singapore: Springer Singapore.