It is generally accepted that collaborative information is important for the performance of recommender systems. It is also generally accepted that if this information is sparser, it impacts recommendation systems negatively. Various approaches have tried to lift this problem by employing side information. However, global patterns that can be provided by clusters of similar items and users or even additional information such as text are often not used together with collaborative information. We study the impact of integrating clustering embeddings, review embeddings, and their combinations with embeddings obtained by a recommender system. We study the performance of this approach across various state-of-the-art recommender system algorithms including graph-based methods. We highlight that graph structures are important with sparser datasets and both, in knowledge graphs with side information as well as in collaborative bipartite graphs. In less sparse datasets, a collaborative bipartite graph is usually sufficient. We also highlight that the improvement of recommendation performance through clustering, particularly evident when combined with review embeddings is most visible on sparser data, while on less sparse data incorporating review embeddings may be sufficient when combined with one of the graph-based methods, or otherwise when combined with clustering in other methods.

Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories.In this paper, we link knowledge graph completion and counterfactual reasoning via our new task CFKGR.We model the original world state as a knowledge graph, hypothetical scenarios as edges added to the graph, and plausible changes to the graph as inferences from logical rules.We create corresponding benchmark datasets, which contain diverse hypothetical scenarios with plausible changes to the original knowledge graph and facts that should be retained.We develop COULDD, a general method for adapting existing knowledge graph embeddings given a hypothetical premise, and evaluate it on our benchmark.Our results indicate that KGEs learn patterns in the graph without explicit training.We further observe that KGEs adapted with COULDD solidly detect plausible counterfactual changes to the graph that follow these patterns.An evaluation on human-annotated data reveals that KGEs adapted with COULDD are mostly unable to recognize changes to the graph that do not follow learned inference rules.In contrast, Chat-GPT mostly outperforms KGEs in detecting plausible changes to the graph but has poor knowledge retention.In summary, CFKGR connects two previously distinct areas, namely KG completion and counterfactual reasoning.

Weakly supervised learning is a popular approach for training machine learning models in low-resource settings.Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations obtained from various weak sources.Recently, many sophisticated approaches have been proposed for robust training under label noise, reporting impressive results.In this paper, we revisit the setup of these approaches and find that the benefits brought by these approaches are significantly overestimated.Specifically, we find that the success of existing weakly supervised learning approaches heavily relies on the availability of clean validation samples which, as we show, can be leveraged much more efficiently by simply training on them.After using these clean labels in training, the advantages of using these sophisticated approaches are mostly wiped out.This remains true even when reducing the size of the available clean data to just five samples per class, making these approaches impractical.To understand the true value of weakly supervised learning, we thoroughly analyze diverse NLP datasets and tasks to ascertain when and why weakly supervised approaches work.Based on our findings, we provide recommendations for future research.1

Evidently, words can have multiple senses.For example, the word mess refers to a place to have food or to a confusing situation.How exactly multiple senses emerge is less clear.In this work, we propose and analyze a mathematical model of the evolution of lexical meaning to investigate mechanisms leading to polysemy.This model features factors that have been discussed to impact the semantic processing and transmission of words: word frequency, nonconformism, and semantic discriminability.We formally derive conditions under which a sense of a word tends to diversify itself into multiple senses that coexist stably.The model predicts that diversification is promoted by low frequency, a strong bias for nonconformist usage, and high semantic discriminability.We statistically validate these predictions with historical language data covering semantic developments of a set of English words.Multiple alternative measures are used to operationalize each variable involved, and we confirm the predicted tendencies for twelve combinations of measures.