  In general, recommender systems are designed to provide personalized items to
a user. But in few cases, items are recommended for a group, and the challenge
is to aggregate the individual user preferences to infer the recommendation to
a group. It is also important to consider the similarity of characteristics
among the members of a group to generate a better recommendation. Members of an
automatically identified group will have similar characteristics, and reaching
a consensus with a decision-making process is preferable in this case. It
requires users-items and their rating interactions over a utility matrix to
auto-detect the groups in group recommendations. We may not overlook other
intrinsic information to form a group. The textual information also plays a
pivotal role in user clustering. In this paper, we auto-detect the groups based
on the textual similarity of the metadata (review texts). We consider the order
in user preferences in our models. We have conducted extensive experiments over
two real-world datasets to check the efficacy of the proposed models. We have
also conducted a competitive comparison with a baseline model to show the
improvements in the quality of recommendations.
