Multi-modal adversarial autoencoders for recommendations of citations and subject labels

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model

Recommendations for item set completion: On the semantics of item co-occurrence with data sparsity, input size, and input modalities

We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender performance. We also investigate data sparsity through a pruning parameter and the influence of using additional metadata. As recommender models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. This outcome means that the semantics of item co-occurrence is an important factor. The simple item co-occurrence model is a strong baseline for citation recommendation. However, autoencoders have the advantage to enable exploiting additional metadata besides the partial item set as input and achieve comparable performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the result. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit

Can graph neural networks go „online“? An analysis of pretraining and inference

Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising, especially, when all the graph’s nodes and edges are available dur- ing training. When unseen nodes and edges are inserted after training, it is not yet evaluated whether up-training or re-training from scratch is preferable. We construct an experimental setup, in which we insert previously unseen nodes and edges after training and conduct a limited amount of inference epochs. In this setup, we compare adapting pretrained graph neural networks against retraining from scratch. Our results show that pretrained models yield high accuracy scores on the unseen nodes and that pretraining is preferable over retraining from scratch. Our experiments represent a first step to evaluate and develop truly online variants of graph neural networks

Lifelong learning in evolving graphs with limited labeled data and unseen class detection

Large-scale graph data in the real-world are often dynamic rather than static. The data are changing with new nodes, edges, and even classes appearing over time, such as in citation networks and research-and-development collaboration networks. Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data. In this work, we employ a two-step procedure to explore how GNNs can be incrementally adapted to new unseen graph data. First, we analyze the verge between transductive and inductive learning on standard benchmark datasets. After inductive pretraining, we add unlabeled data to the graph and show that the models are stable. Then, we explore the case of continually adding more and more labeled data, while considering cases, where not all past instances are annotated with class labels. Furthermore, we introduce new classes while the graph evolves and explore methods that automatically detect instances from previously unseen classes. In order to deal with evolving graphs in a principled way, we propose a lifelong learning framework for graph data along with an evaluation protocol. In this framework, we evaluate representative GNN architectures. We observe that implicit knowledge within model parameters becomes more important when explicit knowledge, i.e., data from past tasks, is limited. We find that in open-world node classification, the data from surprisingly few past tasks are sufficient to reach the performance reached by remembering data from all past tasks. In the challenging task of unseen class detection, we find that using a weighted cross-entropy loss is important for stabilit

Linked Data-Driven Smart Spaces

In this paper, we present an approach to exploit Linked Data in Smart Spaces, doing more than just using RDF to represent informa- tion. In particular, we rely on knowledge stored in DBpedia1, a dataset in the Web of Data. We also provide a platform to implement such an approach and a eTourism use case, both developed in collaboration with a mobile operator. Finally, we provide also a performance evaluation of the main component of the platform

Semantic Annotation and Classification in Practice

TellMeFirst uses linked data to classify and enrich textual documents written in English and Italian. This work describes the use case of a telecommunications operator that has adopted TellMeFirst to add value to its mobile services

Semantic Annotation and Classification in Practice

TellMeFirst uses linked data to classify and enrich textual documents written in English and Italian. This work describes the use case of a telecommunications operator that has adopted TellMeFirst to add value to its mobile services