![]() For instance, people find it more entertaining to visit restaurants, picnic spots, trip sites, or watch movies in groups. However, people are more social, and activities in group become an important part of daily life. Most RS were designed to provide recommendations for individual users. Hybrid recommender systems combine the recommendations of various approaches, and then recommend Top-k items. The CF based methods consider like-minded users and then recommend items by aggregating the preferences of similar users, while content-based models perform recommendations based on similarity of items that the user has interacted with in the past. Generally, the existing schemes can be categorized as Collaborative Filtering (CF), Content Based Filtering, and Hybrid Models. The existing literature takes into account the aforementioned factors to improve the recommendation quality. Numerous factors are involved while computing recommendation for a user, such as a user’s interests, mood, tastes, and similarity with other users, to name a few. ![]() After the announcement of Netflix Prize, RS have received great attention in industries and academia. Recommender Systems (RS) are mathematical models developed in late 90s to compute recommendations for a user that are closely related to the user’s preferences. Information overload is an increasing problem of knowledge engineering that cannot be ignored as users are more interested in finding only relevant information. The information on websites is overwhelming due to which users often find it difficult to access the content of their choice. The last two decades have witnessed a growth in data due to increased use of online applications including e-commerce, online social networks, and multimedia streaming. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. We demonstrate the usefulness of our proposed framework using a movies data set. ![]() Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. For instance, a person may have different preferences in watching movies with friends than with family. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. ![]()
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