In recommendation systems, cold-start user recommendation is a challenging problem, where precise recommendations are required for users who have not appeared before. Several existing cold-start user recommendation models adopt domain adaptation to extract information from auxiliary source domains to assist the recommendations on the target domain. In this article, we propose that the cold-start user recommendation problem can be formulated by the heterogeneous domain adaption approach. We determine a transformation of user features, e.g., user social relations and historical interactions between warm users and their interested items, into a latent space so that the loss function is set by user feature reconstruction and by feature and distribution matching in the heterogeneous domains. The resulting optimization problem can be solved by matrix eigendecomposition, and the cold-start users’ preferences can thus be obtained. We also extend the proposed model using neural networks. We perform extensive experiments on several real-world datasets, and the results in terms of Precision, Recall, NDCG, and Hit Rate verify the effectiveness of the proposed model.

In recommendation systems, the cold-start issue is a long-standing problem where no historical interaction records are given for certain users or items. Under this circumstance, recommendations for new users or new items become challenging. To address this problem, most existing approaches seek to discover a latent common space for users and items. However, these methods require a strong assumption that a shared space exists where the distributions of users and items are identical, which may limit the recommendation performance. In this article, we propose a novel model called Feature Matching Machine (FMM) to learn latent informative user and item representations. Different from previous methods, for warm users (or items), FMM learns two kinds of latent features, i.e., one is constructed by a hypergraph auto-encoder based on historical interactions between users and items, and the other is built by a multi-layer perceptron based on users (or items). Subsequently, FMM matches these two latent feature representations so as to discover the relationships across users (or items) and cold-start items (or users). We conduct extensive experiments on several real-world datasets and compare the proposed method with well-known baseline methods. Promising results demonstrate the effectiveness and efficiency of the proposed model.

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users’ positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method.