# Cross-Modal Manifold Propagation for Image Recommendation
## 2 Related Work
### Collaborative Filtering and Visual Recommendation  
Many research efforts are put into personalized content mining to match relevant multimedia content for users' personalized requirements. Representative user-image correlation estimation methods are built on collaborative filtering (CF) [3,9,10] or content-based (CB) learning [11, 12, 13]. CF recommends others' content to a specific user relying on common interactions among them, whose quality is substantially influenced by their historical records. NBI [14] measures pairwise correlation relying on resource-allocation dynamics, in which bipartite representation extracts intrinsic relationships of network for personalized recommendation. [PERSON] et al. [9] proposed a generalized flexible autoencoder in view of denoising to embed users and items for recommendations. [PERSON] et al. [15] explored the embedding efficiency of CF models and proposed discrete collaborative filtering to maintain inherent user-image correlation with Hamming similarity-based constraint. It produces compact binary embeddings by the balanced and uncorrelated coding constraints resulting in an apparent improvement on recommendation performance. To address cold starts and data sparsity, cross-network collaboration [16] is proposed by paralleling an auxiliary network that integrates user embeddings propagated from the auxiliary network to promote collaborative recommendation performance. [PERSON] et al. [10] introduced a hierarchical attention mechanism into modeling users' interests. In a tower structure, the component-level attention module selects informative components, and the item-level attention module learns to score item preferences. The attention provides a detailed contribution of each component in modeling users' interests, which effectively enhances recommendation performance. GCN [17] propagates users' interest with graph convolution on heterogeneous interaction graph. NeuMF [1] proposes nonlinear interaction mapping on pairwise latent embeddings of users and items for personalized recommendation. [PERSON] et al. [3] investigated nonlinear and higher-order relationships among items by nonlinear neural networks to capture the complicated effects of users in decision making for recommendations. NGCF [4] explicitly  
Figure 2: Framework of proposed cross-modal collaborative manifold propagation involving two modules, interest-based (blue) and visual (green) inference modules for image recommendation. encoded high-order interactions in embeddings to promote representative capability on users' interests. The progressively improved recommendation performance by these CF models verified the effectiveness of graph-based propagation in modeling users' interests. However, graph-based propagation is still subject to sparse interactions. Auxiliary information is highly required to bridge the information gap for propagation. By considering visual information, content-based recommendations [11] aim to recommend items to users on the basis of matching between item descriptions and user interest profiles. [PERSON] et al. [12] measured content similarity with tag frequencies to jointly build user and image profiles for recommendation. LASSO regressor was employed in [13] to emphasize discriminative contents on each user's preference for matching users and visual contents. [PERSON] et al. [18] modeled image collections using group sparse reconstruction and measured their dynamic similarity to users' historical records for image collection recommendation. [PERSON] et al. [19] proposed inferring users' interests from posted images by analyzing visual content and aggregating them together. [PERSON] et al. [20] narrowed the semantic gap by producing a joint semantic space of visual attributes and image indexing. They integrated the feature space with spectral hashing resulting in performance improvement on image matching. A hybrid model [21] implemented item-based collaborative filtering involving latent semantic embeddings.