A cross-domain group recommender system with a generalized aggregation strategy
Ruxia Liang, Qian Zhang, Jie Lü, Guangquan Zhang, Jian‐qiang Wang · Developments of Artificial Intelligence Technologies in Computation and Robotics · 2020
Developing group recommender systems has been a vital requirement due to the prevalence of group activities. However, existing group recommender systems still suffer from data sparsity problem because they rely on individual recommendation methods with a predefined aggregation strategy. To solve this problem, we propose a cross-domain group recommender system with a generalized aggregation strategy in this paper. A generalized aggregation strategy is developed to build group profile in the target domain with the help of individual preferences extracted from a source domain with sufficient data. By adding the constraints between the individual preference and the group profile, knowledge is transferred to assist in the group recommendation task in the target domain. Experiments on a real-world dataset justify the effectiveness and rationality of our proposed cross-domain recommender systems. The results show that we increase the accuracy of group recommendation on different sparse ratios with the help of individual data from the source domain.