Abstract
There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend \(top-k\) items to a group to achieve an object such as minimizing the maximum disagreement between group members, according to a chosen semantics of group satisfaction. We consider the complementary problem of how to form groups such that the users in the formed groups are as even satisfied with the suggested \(top-k\) recommendations as possible. Thanks to emerging crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower, we have easy access to workers’ \(top-N\) recommendation. Here dealing with the ranking data is a big challenge, as quite few methods to solve this issue. We assume that the recommendations will be generated according to minimize the maximum disagreement between group users utilizing the kendall distance. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to minimize the maximum disagreement. Furthermore, we develop efficient algorithms for group formation under the minmax object. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on both real and synthetic data sets.
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References
Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: ICDE, pp. 49–60 (2016)
Song, T., Tong, Y., Wang, L., She, J., Yao, B., Chen, L., Xu, K.: Trichromatic online matching in real-time spatial crowdsourcing. In: ICDE, pp. 1009–1020 (2017)
Crossen, A., Budzik, J., Hammond, K.J.: Flytrap: intelligent group music recommendation. In: IUI, pp. 184–185. ACM (2002)
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_20
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: a recommender system for groups of users. In: Prinz, W., Jarke, M., Rogers, Y., Schmidt, K., Wulf, V. (eds.) ECSCW 2001. Springer, Heidelberg (2001). https://doi.org/10.1007/0-306-48019-0_11
Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. PVLDB 2(1), 754–765 (2009)
Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., Bernier, C.: Analysis of strategies for building group profiles. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 40–51. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13470-8_6
Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: RecSys., pp. 111–118 (2010)
Pizzutilo, S., De Carolis, B., Cozzolongo, G., Ambruoso, F.: Group modeling in a public space: methods, techniques, experiences. In: AIC., pp. 175–180 (2005)
Ntoutsi, E., Stefanidis, K., Nørvåg, K., Kriegel, H.-P.: Fast group recommendations by applying user clustering. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 126–140. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34002-4_10
She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: SIGMOD, pp. 1629–1643 (2015)
Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web 19(6), 1151–1177 (2016)
She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)
Chen, J., Chen, X., Song, X.: Bidder’s strategy under group-buying auction on the internet. IEEE SMC 32(6), 680–690 (2002)
Mallows, C.L.: Non-null ranking models. I. Biometrika 44(2), 114–130 (1957)
Plackett, R.L.: The analysis of permutations. Roy. Stat. Soc. 24(2), 193–202 (1975)
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Gao, Y., Cai, W., Liang, K. (2017). Group Formation Based on Crowdsourced Top-k Recommendation. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_20
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DOI: https://doi.org/10.1007/978-3-319-69781-9_20
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