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In general, recommender systems are designed to provide personalized items toa user. But in few cases, items are recommended for a group, and the challengeis to aggregate the individual user preferences to infer the recommendation toa group. It is also important to consider the similarity of characteristicsamong the members of a group to generate a better recommendation. Members of anautomatically identified group will have similar characteristics, and reachinga consensus with a decision-making process is preferable in this case. Itrequires users-items and their rating interactions over a utility matrix toauto-detect the groups in group recommendations. We may not overlook otherintrinsic information to form a group. The textual information also plays apivotal role in user clustering. In this paper, we auto-detect the groups basedon the textual similarity of the metadata (review texts). We consider the orderin user preferences in our models. We have conducted extensive experiments overtwo real-world datasets to check the efficacy of the proposed models. We havealso conducted a competitive comparison with a baseline model to show theimprovements in the quality of recommendations.

Auto-detecting groups based on textual similarity for group recommendations