Precedence Mining in Group Recommender Systems
We extend the Precedence mining model for personal recommendation as outlined in Parameswaran et.al.,  in three different ways. Firstly, we show how precedence mining model can be used for recommending items of interest to a group of users (group recommendation) and compare and contrast our model with traditional group recommendation models like collaborative and Hybrid. Secondly, we extend the precedence mining model to incorporate ratings for items and experimental results show that the goodness of recommendation is improved. The third extension is related to the issue of new items being ignored which is a fundamental problem plaguing collaborative and precedence mining algorithms. When recommendations are based on other users interests (like in Collaborative recommender systems) the possibility of not recommending a new item which has not been consumed by many of the users is high though the new item may be of interest to the target user. We outline two models, Vector precedence-mining and Hybrid precedence-mining that addresses this issue.
KeywordsPrecedence Probability Recommender System Recommendation Algorithm Traditional Group Group Recommender
- 1.Amer-Yahia, S., Roy, S.B., Chawla, A., Das, G., Yu, C.: Group recommendation: Semantics and efficiency. PVLDB 2(1), 754–765 (2009)Google Scholar
- 2.Breese, J., Heckermanx, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI (1998)Google Scholar
- 4.Padmanabhan, V., Kiran, P., Sattar, A.: Group recommender systems: Some experimental results. In: 5th International Conference on Agents and Artificial Intelligence (ICAART 2013), Barcelona, Spain, pp. 370–376 (2013)Google Scholar
- 6.Parameswaran, A.G., Koutrika, G., Bercovitz, B., Garcia-Molina, H.: Recsplorer: recommendation algorithms based on precedence mining. In: SIGMOD Conference, pp. 87–98 (2010)Google Scholar
- 7.Seko, S., Yagi, T., Motegi, M., Muto, S.Y.: Group recommendation using feature space representing behavioral tendency and power balance among members. In: RecSys, pp. 101–108 (2011)Google Scholar