Existing multimodal recommenders largely rely on late fusion, where modalities are separately encoded and aligned before fusion. Such designs work well with complete content but become fragile in practice: modalities are often missing, alignment breaks down, and imputation adds complexity with limited benefit. We propose EFMRec, a missing-aware early-fusion framework that follows a relaxed principle of leveraging modalities when available and ignoring them when absent. By exploiting pretrained multimodal models, EFMRec projects available modalities into a shared semantic space and aggregates them through a missing-aware early fusion, producing unified representations without reconstruction or auxiliary losses. These representations are further propagated via a GCN-based architecture and integrated with a collaborative filtering backbone to jointly model multimodal and collaborative signals. Experiments on three benchmarks demonstrate that EFMRec consistently outperforms strong baselines under both full- and missing-modality settings, highlighting its robustness to modality incompleteness.

This paper is concerned with the one class classification(OCC) problem. By introducing the vector-valued function with regularizations in Y-valued Reproducing Hilbert Kernel Space(RHKS), we build an unsupervised classifier and discover the outliers and inliers simultaneously. Manifold regularization is employed to preserve the local similarity of data in input space. Experimental results of the proposed and comparing methods on OCC data sets demonstrate the performance of the proposed algorithm.

Predicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both "users" and "items", e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.