The exponential growth of published papers in digital libraries has made finding relevant papers increasingly challenging for researchers. Numerous citation recommendation models have been developed to mitigate this issue and assist researchers in identifying pertinent works. Unfortunately, many of these models struggle to generate high-quality recommendations because they fail to capture diverse relationship patterns and effectively model the multifaceted relationships present in citation networks. Additionally, these models lack robustness in learning the representations of research papers. To address these limitations, we propose an innovative model that integrates graph embeddings to better understand hidden relationships in bibliographic networks and model multi-fold relations using the MRotatE framework. Our model first leverages graphical feature embeddings and content-based representations through MRotatE and SPECTER document embedding to construct initial representations of both query and candidate papers. These representations are then used in a generator and discriminator, which undergo adversarial training, ultimately resulting in improved recommendation outcomes. We evaluated our model against several baselines using three real-world datasets, and the results demonstrate that our approach outperforms existing models. The code for this project is publicly available online. 1

Digital publishing’s exponential growth has created vast scholarly collections. Guiding researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for unlocking hidden knowledge. However, current methods focus on external links between concepts, ignoring the rich information within individual papers. Challenges like insufficient multi-relational data, name ambiguity, and cold-start issues further limit existing KG-based methods, failing to capture the intricate attributes of diverse entities. To solve these issues, we propose GLAMOR, a robust KG framework encompassing entities e.g., authors, papers, fields of study, and concepts, along with their semantic interconnections. GLAMOR uses a novel random walk-based KG text generation method and then fine-tunes the language model using the generated text. Subsequently, the acquired context-preserving embeddings facilitate superior top@k predictions. Evaluation results on two public benchmark datasets demonstrate our GLAMOR’s superiority against state-of-the-art methods especially in solving the cold-start problem.

The representation of real-life problems by using k-partite graphs introduced a new era in Machine Learning. Moreover, the merge of virtual and physical layers through Location Based Social Networks (LBSN s) offers a different meaning into the constructed graphs. To this point, multiple models introduced in literature that aim to support users with personalized recommendations. These approaches represent the mathematical models that aim to understand users' behaviour by finding patterns on users' check-ins, reviews, ratings, friendships, etc. With this paper we describe and compare 20 of those state-of-the-art deep learning models to bring into the surface some of their strengths and shortcomings. First, we categorize them according to: data factors or features they use, data representation, methodologies used and recommendation types they support. Then, we highlight the existing limitations that tackles their performance. Finally, we introduce research trends and future directions.

The rapid growth of Location-based Social Networks (LBSNs) has lead to the generation of massive datasets which are collected in an exponential rate. The collected information may be used to facilitate users' needs with recommendations related to their past preferences. Many recommendation models were introduced in the literature, which learn by the history of users and provide recommendations for Points-of-Interest. Unfortunately, most of them ignore the relation existing among the temporal properties, the spatial attributes and the periodicity of the check-ins. In this work, we present a novel methodology, named JLGE, that combines all aforementioned factors into one unified approach which facilitates POI recommendations. In particular, the model jointly learns the embeddings of six informational graphs i.e., two unipartite (user-user and POIPOI) and four bipartite (user-location, user-time, location-user, and location-time) into the same latent space and personalize the recommendations based on these embeddings. We have experimentally evaluated the accuracy of our model using two real-world datasets in terms of the top-n POIs recommendations. The performance evaluation results indicate a significant improvement in accuracy, in comparison to another state-of-theart graph-based approach.

Recently, location-based social networks (LBSNs) gave the opportunity to users to share geo-tagged information along with photos, videos, and SMSs. Recommender systems can exploit this geographic information to provide much more accurate and reliable recommendations to users. In this paper, we present and compare 16 real life LBSNs, bringing into surface their advantages/ disadvantages, their special functionalities, and their impact in the mobile social Web. Moreover, we describe and compare extensively 43 state-of-the-art recommendation algorithms for LBSNs. We categorize these algorithms according to: personalization type, recommendation type, data factors/features, problem modeling methodology, and data representation. In addition to the above categorizations which cannot cover all algorithms in an integrated way, we also propose a hybrid k-partite graph taxonomy to categorize them based on the number of the involved k-partite graphs. Finally, we compare the recommendation algorithms with respect to their evaluation methodology (i.e., datasets and metrics) and we highlight new perspectives for future work in LBSNs.

Online social networks have attracted users' attention in the last decade. Recommendation services constitute a critical functionality of such social platforms: users receive recommendations about resources (documents, pieces of music) and potential friends (people with the same interests). Recently, technological progressions in smart phones enabled the exploitation of geographical data information in social networks. Users can now receive recommendations about new Points of Interest (POIs), and new activities in POIs. Eventually, Location-based Social Networks (LBSNs) may become the 'Next Big Thing' of the Internet industry. This paper surveys the related work and current state-of-the-art algorithms in LBSNs. We also provide three new perspectives that concern recommendations in LBSNs: time-awareness, user's privacy issues, and explainability of recommendations. We present the latest work in LBSNs by comparing real systems and by categorizing them in multiple ways (platforms, personalization, etc.).