Auto-detecting groups based on textual similarity for group recommendations
This work addresses the challenge of aggregating individual preferences for group recommendations in recommender systems, but it is incremental as it builds on existing methods by incorporating textual similarity.
The paper tackles the problem of generating group recommendations by auto-detecting groups based on textual similarity from review metadata, and it shows improvements in recommendation quality through experiments on real-world datasets and competitive comparisons with a baseline model.
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.