Evaluation of Group Modelling Strategy in Model-Based Collaborative Filtering Recommendation

A hybrid group-based movie recommendation framework with overlapping memberships

“…We observe optimum value when group size is 10, which is same as of Nawi et al . [ 25 ] where it is calculated through elbow method. For the group size greater than 15, the HTGF shows almost constant results better than existing schemes which experience performance degradation above group size 10, and hence their graphs are not included in the figure.…”

“…The matrix splitting and estimation procedure of ALS results in low accuracy as compared to SVD that splits the matrices into three sub-matrices. Moreover, the ALS and SVD experience degradation in recommendation quality as they fail to capture the implicit preferences of individuals participating in a group [ 25 ]. However, our proposed model based on NCF takes into account the latent feature vectors of users and movies which minimizes the error rate as compared to ALS and SVD.…”

“…However, the proposed model predicted a limited set of movies with less accuracy [ 23 ]. The authors in [ 25 ] evaluated aggregation strategies: average and most pleasure, on two baseline models Alternating Least Squares (ALS) and Singular Value Decomposition (SVD). For clustering, they applied cosine similarity, and for recommendation they used two baseline models.…”

“…MovieLens 1M is a standard dataset widely used by researchers in recommendation systems. We compare our work with baseline models i.e., ALS and SVD, which are used by most of the existing schemes for comparisons in group recommendation scenarios [ 25 ] The ALS algorithm factorizes a given matrix R into two factors U and V , such that R ≈ U T V . Here, U represents set of users and V represents set of movies.…”

“…Due to the implicit feature learning of neural networks, researchers have applied these models for solving recommendation problems [ 24 ]. Most of the existing model-based schemes utilize matrix-factorization methods whose estimation based mechanisms result in low prediction accuracy in cold start and sparsity scenarios [ 25 – 27 ]. In [ 28 ], the authors captured the fairness among group members by using SVD++ model.…”