Comprehensive Study on Usage of Multi Objectives in Recommender Systems
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Abstract
Recommender systems have changed its purview from prediction accuracy oriented to finding more relevant and useful recommendations to user. “Usefulness” of items are different in different applications. This paper summarizes the works that have been done in this direction. Personalization, context awareness, multiple objectives of recommendations and evaluation metrics are reviewed in this paper.
Keywords
Context-awareness Multi-objective Personalization Recommender systemsReferences
- 1.Herlocker, J.L.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRefGoogle Scholar
- 2.Vargas, S.J., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems. ACM (2011)Google Scholar
- 3.Zhou, T., et al.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Nat. Acad. Sci. 107(10), 4511–4515 (2010)Google Scholar
- 4.Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)CrossRefGoogle Scholar
- 5.Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Recommender Systems Handbook, pp. 881–918. Springer, US (2015)Google Scholar
- 6.Belém, F.M., et al.: Beyond relevance: explicitly promoting novelty and diversity in tag recommendation. ACM Trans. Intell. Syst. Technol. (TIST) 7(3), 26 (2016)Google Scholar
- 7.Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the Fourth ACM Conference on Recommender Systems. ACM (2010)Google Scholar
- 8.Kotkov, D., Veijalainen, J., Wang, S.: Challenges of serendipity in recommender systems. In: WEBIST 2016: Proceedings of the 12th International Conference on Web Information Systems and Technologies, vol. 2. SCITEPRESS (2016). ISBN 978-989-758-186-1Google Scholar
- 9.Sugiyama, K., Kan, M.Y.: Towards higher relevance and serendipity in scholarly paper recommendation. ACM SIGWEB Newsletter (2015)Google Scholar
- 10.Lathia, N., et al.: Temporal diversity in recommender systems. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2010)Google Scholar
- 11.Yamaba, H., et al.: On a serendipity-oriented recommender system based on folksonomy. Artif. Life Robot. 18(1–2), 89–94 (2013)Google Scholar
- 12.Venkataraman, D., Gangothri, V., Saranya, S.: A comprehensive review of recommender system. Int. J. Appl. Eng. Res. 10, 13909–13919 (2015)Google Scholar
- 13.O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces. ACM (2005)Google Scholar
- 14.Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems. ACM (2007)Google Scholar
- 15.Kapoor, K., et al.: I like to explore sometimes: adapting to dynamic user novelty preferences. In: Proceedings of the 9th ACM Conference on Recommender Systems. ACM (2015)Google Scholar
- 16.Woerndl, W., Schueller, C., Wojtech, R.: A hybrid recommender system for context-aware recommendations of mobile applications. In: 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE (2007)Google Scholar
- 17.Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 191–226. Springer, US (2015)Google Scholar
- 18.Nasraoui, O., Pavuluri, M.: A context ultra-sensitive approach to high quality Web recommendations based on Web usage mining and neural network committees. In: 3rd International Workshop on Web Dynamics in Conjunction with the 13th International World Wide Web Conference New York City, New York, USA, 18 May 2004Google Scholar
- 19.Pu, P., Chen, L., Rong, H.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap. Inter. 22(4), 317–355 (2012)CrossRefGoogle Scholar
- 20.Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Syst. Appl. 23(3), 329–342 (2002)Google Scholar
- 21.Durao, F., Dolog, P.: Extending a hybrid tag-based recommender system with personalization. In: Proceedings of the 2010 ACM Symposium on Applied Computing. ACM (2010)Google Scholar
- 22.Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Advanced Learning Technologies, 2008. ICALT’08. Eighth IEEE International Conference on. IEEE (2008)Google Scholar
- 23.He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)CrossRefGoogle Scholar
- 24.Guan, Y., et al.: Preference of online users and personalized recommendations. Physica A Stat. Mech. Appl. 392(16), 3417–3423 (2013)Google Scholar
- 25.Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
- 26.Yedugiri, K.B., Chandni, S.P., Raj, S., Souparnika, S.: Recommender systems—a deeper insight. Int. J. Appl. Eng. Res. 9, 28521–28531 (2014)Google Scholar
- 27.Zuo, Y., et al.: Personalized recommendation based on evolutionary multi-objective optimization research frontier. IEEE Comput. Intell. Mag. 10(1), 52–62 (2015)Google Scholar
- 28.Horváth, T., de Carvalho, A.C.: Evolutionary computing in recommender systems: a review of recent research. Nat. Comput. 1–22 (2016)Google Scholar
- 29.Smeaton, A.F., Callan, J.: Personalisation and recommender systems in digital libraries. Int. J. Digit. Libr. 5(4), 299–308 (2005)CrossRefGoogle Scholar
- 30.Ribeiro, M.T., et al.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the sixth ACM conference on Recommender systems. ACM (2012)Google Scholar
- 31.Wang, J., et al.: Diversified recommendation incorporating item content information based on MOEA/D. In: 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE (2016)Google Scholar
- 32.Boumaza, A., Brun, A.: From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. ACM (2012)Google Scholar
- 33.Ribeiro, M.T., et al.: Multiobjective pareto-efficient approaches for recommender systems. ACM Trans. Intell. Syst. Technol. (TIST) 5(4), 53 (2015)Google Scholar
- 34.Wang, S., et al.: Multi-objective optimization for long tail recommendation. Knowl. Based Syst. 104, 145–155 (2016)Google Scholar
- 35.Bingrui, G., et al.: NNIA-RS: a multi-objective optimization based recommender system. Physica A Stat. Mech. Appl. 424, 383–397 (2015)Google Scholar
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