Cross-domain recommender systems (CDRSs) enhance recommendations by transferring knowledge of overlapping users across two domains. Deep canonical correlation analysis (DCCA) shows promising results in CDRSs by maximizing correlations between representations of overlapping users, enabling cross-domain knowledge transfer that depends on the degree of relationship between domains. As a result, DCCA selectively shares only relevant knowledge, alleviating the problem of noisy representation found in traditional CDRSs, where they transfer knowledge regardless of the correlation strength between domains. Although DCCA is used for user transfer, item transfer, referring to the transfer of explicit knowledge of the same items between domains, is impossible due to the absence of overlapping items to facilitate direct knowledge transfer. Meanwhile, graph neural networks (GNNs) embed users and items from separate user and item graphs in each domain. Therefore, better representations are obtained from captured complex relationships and collaborative signals. To construct graphs of overlapping items, latent linkages among items between domains could be discovered by the neural topic model (NTM), forming new graphs representing the latent relationships. Therefore, COLANet, a GNN-based CDRS, is proposed to solve the DCCA limitation on item transfer by proposing the extraction of item representations that do not exist in another domain using latent characteristics. First, user-user graphs are constructed using user similarity, and the item-topic graph is constructed using latent topics learned from item descriptions with NTM. Hence, user and item graphs of each domain are constructed separately, preventing domain relationship misalignment. Second, these graphs are fed to GNN to obtain user and item representations. Third, these representations are fed to DCCA to transfer knowledge between user-user and item-item. Finally, correlated user and item representations of each domain are used to predict ratings. The experiments demonstrate that COLANet outperforms the baselines across four pairs of domains, including both similar and different domains.

Many recommender systems leverage Graph Neural Networks to capture user–item relations for delivering recommendations. However, the representation of nodes heavily relies on neighbors, causing limited neighbor nodes (sparse nodes) to lack expressive representation. Existing works discovered latent neighbors for sparse nodes but ignored node sparsity, resulting in the neighbor misallocation problem due to overlooking quality and quantity aspects. We propose discovering high-quality latent neighbors by progressively transferring knowledge from dense nodes by categorizing user–item interactions into four quadrants (dense user–dense item, dense user–sparse item, sparse user–dense item, and sparse user–sparse item). We leverage the node sparsity to determine the optimal quantity of latent neighbors. We propose a Domain Adaptation Network for transferring knowledge from dense to sparse quadrants without encountering the domain misalignment problem arising from the distinct representations between dense and sparse quadrants. An Enrichment Network is proposed to address the inexpressive representation problem due to limited observed interactions by enriching the sparse node representation. A Heterogeneous Graph Neural Network architecture is proposed to capture multiple relations between dense/sparse users and items. Experimental results on three benchmark datasets demonstrate the superiority of the proposed method over Graph Neural Network baselines, both with and without latent neighbors.

Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction due to the improper rating range problem. Rating conversion methods are proposed to solve this problem over the past few years. To propose rating conversion method, each user’s preference or rating pattern is needed. Some studies adopt the idea from translation-based embedding model and represent user’s preference in graph form. Although some studies represent users, items, and relations in embedding vector form, their representation may be improper and inaccurate if the rating pattern of each user is not in the same range. These vectors still suffer from the improper rating range as well. In this work, we propose a translation-based embedding model with rating conversion in RSs. We aim to solve the improper rating range problem in translation-based embedding model. Our challenges are 1) representing the relation (rating) between a pair of user and item in vector form, instead of scalar form and 2) dealing with rating conversion of user’s rating in vector form. The FilmTrust and MovieLens dataset are used in experiments comparing the proposed method with the existing methods. The evaluation showed that the proposed rating conversion method provides better accuracy results in term of both rating prediction and ranking recommendation.

Time-Aware Recommender System (TARS) is a type of Context-Aware Recommender System that consider time for predicting the rating of the target item. In the current TARS, they give a high importance to the data that is nearby time to the current time or nearby time to the target user data but did not consider preference change, or concept driven, of the target user. Therefore, the result of the current TARS may not good enough. This paper proposes the TARS that can detect the individual preference driven by using FCM algorithm and entropy to find the preference change in the rating timeline. Then, we select the period in the past of the target user that has a similar preference to the current preference period to find the neighbors and predict the target item's rating. The proposed method does not necessary use the entire user data in the past but uses only the data in the past period of the target user that is similar to the current period for prediction. In the experiment, the proposed method is compared with the two current TARS methods on MovieLens dataset. The evaluation results can be confirmed that the proposed method provides more accuracy and coverage than the two current methods. Moreover, the proposed method uses smaller data than the other two methods. Thus, this makes the calculation time faster.

Collaborative filtering is a recommender technique that recommends items to an individual user based on the item ratings provided by similar users. However, current systems often do not acquire sufficient ratings to be able to generate recommendations. Trust-based recommender systems have been proposed that use additional trust values in generating recommendations. In this paper, we propose a trust-based ant recommender with two main improvements. First, we achieve better selection of higher-quality raters by our proposed trust-calculation method and an improved pheromone-update mechanism. Second, we can improve the prediction step by converting raters' ratings into a target user's perspective view and considering the influence level of each rater on the active user. The Epinions dataset was used in experiments comparing the proposed method with the ALT-BAR method. The evaluation showed that the proposed method provides better results in term of both accuracy and coverage.

Context-aware recommender systems that provide better recommendations for users by using their rating history in different situations have been proposed. Because incorporating all contextual information can make the data sparser and degrade the prediction accuracy, most context-aware methods focus on detecting and using only the most effective contextual factors. However, in addition to accuracy, the diversity of the recommendation is also a key to improving users' satisfaction with recommendation results. Moreover, most context-aware techniques have not considered directly the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This paper proposes a latent probabilistic model to incorporate the contextual information. By adopting a binary particle-swarm optimization technique, the relevant contextual factors for user classes and item classes are identified and incorporated into the model. We optimize our model for two cases, namely considering accuracy alone and considering the trade-off between accuracy and diversity. An evaluation shows that our proposed model performs better than 1) a model that considers only the relation of context to users alone or items alone, 2) a model that exploits all contextual factors, and 3) the traditional context-aware recommendation method.

Recommender systems (RS) are software tools that provide personalized recommendations of relevant items to individual users. However, most of them do not take into account additional contextual information that may affect user preferences, such as place, time, or weather. Context-aware recommender systems (CARS) have been proposed to solve this problem by providing recommendations for users based on their rating history in different situations. Although most have tried to identify the contextual variables that have the greatest effect on rating accuracy, they have not directly considered the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This work proposes a latent probabilistic model for contextual recommendation by extending the flexible mixture model to incorporate different contextual factors. This model has the flexibility to adjust the effects of contextual factors on users and items according to a variety of context-user-item relations to suit specific situations. Our evaluation has shown that the proposed model's recommendations are more accurate than those made by both latent probabilistic models and collaborative filtering-based CARS.

This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.

Currently, people prefer shopping interesting items from the Internet. Therefore, e-business had installed recommender systems to support users in selecting products as quick as possible. The responsibility of the recommender system is to propose interesting items to the target users in order to gain their interests. Unfortunately, the existing recommender systems have some imperfect function under some certain situations. This paper proposed the INFA that improves the qualities of neighbors by detailing features of users' preferences. Moreover, in the neighbor forming process, the consideration domain of agreements is classified into two independent domains: the positive, and the negative agreements. Furthermore, the MNBT has improved the ability of recommender under the situation of no opinion from neighbors, or no existing neighbors