Abstract
There is a growing interest in context-aware recommendation systems (CARS) due to their ability to enable novelty, dynamism and serendipity in resulting recommendations. The incorporation of different user-contextual parameters is important in CARS. However, the problem of identifying the most optimal subset of user-contextual parameters that add real value to resulting recommendations from CARS remain unresolved. This paper investigates an approach to characterise user-contextual parameters and identify the optimal subset of parameters that enables best results from CARS algorithms. In this paper, two data sets were collected from different people in Africa, each data set had 21 classified user-contexts and corresponding likes. An association rule mining (apriori) algorithm was used to generate rules from the user profiles (user contextual-parameters) from both data sets, to identify user-contextual parameters which are strongly associated with certain user preferences. Five(5)[Hobbies, Mood, Family setup, Religion and Gender] user-contextual parameters out of the 21 were identified as the optimal subset of parameters for the domain and the data sets in this study. This optimal subset was found promising in helping CARS as the parameters which can estimate best the preferences of the users and hence led to best recommendations. This paper contributes a new method of identifying the optimal subset of user-contextual parameters in any domain, which enables CARS lgorithms to produce the most appropriate recommendations for each user.
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References
Aggarwal, C.C.: Recommender Systems. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3
Aghdam, M.H.: Context-aware recommender systems using hierarchical hidden markov model. Phy. A Stat. Mech. Appl. 518, 89–98 (2018)
Andjelkovic, I., Parra, D., O’Donovan, J.: Moodplay: interactive music recommendation based on Artists’ mood similarity. Int. J. Hum. Comput. Stud. 121, 142–159 (2018)
Bahramian, Z., Ali Abbaspour, R., Claramunt, C.: A cold start context-aware recommender system for tour planning using artificial neural network and case based reasoning. Mob. Inf. Syst. (2017)
Campana, M.G., Delmastro, F.: Recommender systems for online and mobile social networks: a survey. Online Soc. Netw. Media 3–4, 75–97 (2017)
Champiri, Z.D., Shahamiri, S.R., Salim, S.S.B.: A systematic review of scholar context-aware recommender systems. Expert Syst. Appl. 42(3), 1743–1758 (2015)
Colombo-Mendoza, L.O., Valencia-García, R., Alor-Hernández, G., Bellavista, P.: Special issue on context-aware mobile recommender systems. Pervasive Mob. Comput. 38, 444–445 (2017)
Colombo-Mendoza, L.O., Valencia-García, R., Rodríguez-González, A., Alor-Hernández, G., Samper-Zapater, J.J.: RecomMetz: a context-aware knowledge-based mobile recommender system for movie showtimes. Expert Syst. Appl. 42(3), 1202–1222 (2015)
Colombo-Mendoza, L.O., Valencia-García, R., Rodríguez-González, A., Colomo-Palacios, R., Alor-Hernández, G.: Towards a knowledge-based probabilistic and context-aware social recommender system. J. Inf. Sci. 44(4), 464–490 (2018)
Eirinaki, M., Gao, J., Varlamis, I., Tserpes, K.: Recommender systems for large-scale social networks: a review of challenges and solutions. Future Gener. Comput. Syst. 78, 413–418 (2018)
Elahi, M., Ricci, F., Rubens, N.: A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20, 29–50 (2016)
Haruna, K., Ismail, M.A., Damiasih, D., Chiroma, H., Herawan, T.: A comprehensive survey on comparisons across contextual pre-filtering, contextual post-filtering and contextual modelling approaches. Telkomnika (Telecommun. Comput. Electron. Control) 15(4), 1865–1874 (2017)
Haruna, K., et al.: Context-aware recommender system: a review of recent developmental process and future research direction. Appl. Sci. 7(12), 1211 (2017)
Horowitz, D., Contreras, D., Salamó, M.: EventAware: a mobile recommender system for events. Pattern Recogn. Lett. 105, 121–134 (2018)
Árnason, J.I., Jepsen, J., Koudal, A., Schmidt, M.R., Serafin, S.: Volvo intelligent news: a context aware multi modal proactive recommender system for in-vehicle use. Pervasive Mob. Comput. 14, 95–111 (2014)
Inibhunu, C., Langevin, S.: Adaptive visualization of complex networks with focalpoint: a context aware level of details recommender system. In: Proceedings of the Human Factors and Ergonomics Society, pp. 233–237 (2016)
Kapoor, K., Kumar, V., Terveen, L., Konstan, J.A., Schrater, P.: I like to explore sometimes: adapting to dynamic user novelty preferences, pp. 19–26 (2015)
Keikha, F., Fathian, M., Gholamian, M.R.: TB-CA: a hybrid method based on trust and context-aware for recommender system in social networks. Manage. Sci. Lett. 5(5), 471–480 (2015)
Kotkov, D., Veijalainen, J., Wang, S.: How does serendipity affect diversity in recommender systems? a serendipity-oriented greedy algorithm. Computing 109, 393–411 (2018)
Kotkov, D., Wang, S., Veijalainen, J.: A survey of serendipity in recommender systems. Knowl. Based Syst. 111, 180–192 (2016)
Lahlou, F.Z., Benbrahim, H., Kassou, I.: Context aware recommender system algorithms: state of the art and focus on factorization based methods. Electron. J. Inf. Technol. 23–37 (2017)
Mu, R., Zeng, X., Han, L.: A survey of recommender systems based on deep learning. IEEE Access 6, 69009–69022 (2018)
Nguyen, T.N., Nguyen, A.T.: Chronological states of viewer’s intentions using hidden Markov models and features of eye movement. EAI Endorsed Trans. Framework Developing Context-aware Syst. 1(1), 1–26 (2014)
Rezaeenour, J., Lesani, F.S.: A context aware recommender system for mobile phone selection using combination of elimination method and analytic hierarchy processing. Iran. J. Inf. Process. Manage. 32(4), 1203–1228 (2016)
Sundermann, C.V., Domingues, M.A., Da Silva Conrado, M., Rezende, S.O.: Privileged contextual information for context-aware recommender systems. Expert Syst. Appl. 57, 139–158 (2016)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3
Unger, M., Bar, A., Shapira, B., Rokach, L.: Towards latent context-aware recommendation systems. Knowl. Based Syst. 104, 165–178 (2016)
Verbert, K., et al.: Context-aware recommender systems for learning : a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)
Villegas, N.M., Sánchez, C., Díaz-Cely, J., Tamura, G.: Characterizing context-aware recommender systems: a systematic literature review. Knowl. Based Syst. 140, 173–200 (2018)
Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)
Yang, Z., Bing, W., Zheng, K., Wang, X., Lei, L.: A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4, 3273–3287 (2016)
Zhang, R., Gao, M., He, X., Zhou, A.: Learning user credibility for product ranking. Knowl. Inf. Syst. 46(3), 679–705 (2016)
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Kavu, T.D., Dube, K., Raeth, P.G., Hapanyengwi, G. (2022). Characterisation of User-Contexts for Context-Aware Recommendation Systems. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_7
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