DAERec-GCA: A Deep Autoencoder-Based Collaborative Filtering Framework with Genre-Channel Alignment

In top-N recommendation, incorporating item-side information can improve ranking quality under sparse user–item interactions; however, common flat concatenation strategies may weaken the structural correspondence between user ratings and item attributes while simultaneously increasing model size. To address this issue, this study proposes DAERec-GCA, a deep autoencoder-based collaborative filtering framework that organizes rating signals and genre information in a genre-channel-aligned two-dimensional representation. The model applies shared weights across genre channels and aggregates channel outputs to generate item scores, enabling side-information integration without the parameter growth associated with flattened genre-aware formulations. The framework was evaluated on MovieLens-100K, 1M, and 10M under a warm-start five-fold cross-validation protocol using ranking-based metrics. In addition, a structured ablation study was conducted against ROnly, Flat1D, GenreProfile, GenreEmbed, and GenreGated, together with a controlled train-side sparsity analysis and a computational profiling analysis covering trainable parameters, epoch time, inference latency, and peak GPU memory. The results show that DAERec-GCA remains competitive across all three datasets and exhibits its clearest advantage under sparse and moderately sparse training conditions. The findings suggest that genre-channel alignment provides a practical trade-off between structural expressiveness, parameter efficiency, and recommendation quality in sparse recommendation settings.