Abstract
With the application of recommender system increasing, the research and application of group recommender have been paid more attention. In the course of group activities, the unknown preferences of users are often affected by other members of the group. However, in the existing group recommender system, this effect is not taken into account. In this paper, we propose a novel recommender model that incorporates the preference interaction in the group recommender into rating predicting process. The model is divided into two parts: self-prediction and preference-interaction, the preference-interaction will be systematically analyzed and illustrated. For every user in the group, we use group activity history information and recommender post-rating feedback mechanism to generate personalized interactive parameters. Thus, it can improve the group’s recommender accuracy. Finally, the model is combined with the collaborative filtering algorithm and compared with the algorithm without the model on the MovieLens dataset. The experiment results show that the model proposed in this paper can improve the accuracy of the group recommender results obviously.
This work is supported by the National Natural Science Foundation of China (61672284, 61373015, 41301407), the Funding of Security Ability Construction of Civil Aviation Administration of China (AS-SA2015/21), the Innovation Funding of Nanjing University of Aeronautics and Astronautics (NJ20160028), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Australian Research Council Discover Project (DP140100104), Linkage Project (LP160100630).
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References
Bobadilla, J., Ortega, F., Hernando, A.: Recommender systems survey. Knowl. Based Syst. 46(1), 109–132 (2013)
Garcia, I., Pajares, S., Sebastia, L., Onaindia, E.: Preference elicitation techniques for group recommender systems. Inf. Sci. 189(8), 155–175 (2012)
Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User Adap. Inter. 16(3), 281–319 (2006)
Aggarwal, C.C.: Recommender Systems: The Textbook. Springer, Heidelberg (2016)
Yin, H., Cui, B.: Spatio-Temporal Recommendation in Social Media. Springer, Singapore (2016)
Boratto, L., Carta, S., Chessa A., et al.: Group recommendation with automatic identification of users communities. In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 547–550. IEEE Computer Society (2009)
Kim, J.K., Kim, H.K., Oh, H.Y., et al.: A group recommendation system for online communities. Int. J. Inf. Manage. 30(3), 212–219 (2010)
Crossen, A., Budzik, J., Hammond, K.J.: Flytrap: intelligent group music recommendation. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, San Francisco, USA, pp. 184–185 (2002)
Yin, H., Cui, B., Chen, L., et al.: A temporal context-aware model for user behavior modeling in social media systems. In: International Conference Proceedings on Management of Data, pp. 1543–1554. Association for Computing Machinery (2014). Special Interest Group on Management of Data
Yin, H., Cui, B., Chen, L., et al.: Dynamic user modeling in social media system. ACM Trans. Inf. Syst. 33(3), 10 (2015)
Yin, H., Cui, B., Chen, L., et al.: Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data 9(3), 1–41 (2015)
Ricci, F., Rokach, L., Shapira, B., et al.: Introduction to Recommender Systems Handbook. Springer, Boston (2011)
Garcia, I., Sebastia, L., Onaindia, E.: On the design of individual and group recommender systems for tourism. Exp. Syst. Appl. 38(6), 7683–7692 (2011)
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: ACM Conference on Recommender Systems, pp. 119–126. ACM (2010)
Mccarthy, J.F., Anagnost, T.D.: MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: ACM Conference on Computer Supported Cooperative Work, pp. 363–372. ACM (2000)
Boratto, L.: Group recommender systems: state of the art, emerging aspects and techniques, and research challenges. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Di Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 889–892. Springer, Cham (2016). doi:10.1007/978-3-319-30671-1_87
O’Connor, M., Cosley, D., Konstan, J.A., et al.: PolyLens: a recommender system for groups of users. In: Proceedings of the seventh Conference on European Conference on Computer Supported Cooperative Work, pp. 199–218. Kluwer Academic Publishers (2001)
Gartrell, M., Xing, X., Lv, Q., et al.: Enhancing group recommendation by incorporating social relationship interactions. In: International ACM SIGGROUP Conference on Supporting Group Work, Group 2010, Sanibel Island, Florida, USA, November, pp. 97–106 (2010)
Campos, L.M.D., Fernández-Luna, J.M., Huete, J.F., et al.: Managing uncertainty in group recommending processes. User Model. User Adap. Inter. 19(3), 207–242 (2009)
Pessemier, T.D., Dooms, S., Martens, L.: Comparison of group recommendation algorithms. Multimedia Tools Appl. 72(3), 2497–2541 (2014)
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Zheng, W. et al. (2017). Group Recommender Model Based on Preference Interaction. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_10
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