# LaSER: Language-Specific Event Recommendation

arXiv: https://arxiv.org/abs/2303.04712 · HTML: https://mlsanity.com/paper/2303.04712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 70 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 27 of 27)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision](https://mlsanity.com/paper/2204.04879/) (2022)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [A Deep Look into Neural Ranking Models for Information Retrieval](https://mlsanity.com/paper/1903.06902/) (2019)
- [EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation](https://mlsanity.com/paper/1905.08794/) (2019)
- [Flexible retrieval with NMSLIB and FlexNeuART](https://mlsanity.com/paper/2010.14848/) (2020)
- [Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata](https://mlsanity.com/paper/1904.05557/) (2019)
- [Multilinguals and Wikipedia Editing](https://mlsanity.com/paper/1312.0976/) (2013)
- [The Tower of Babel Meets Web 2.0: User-Generated Content and its Applications in a Multilingual Context](https://mlsanity.com/paper/1904.01689/) (2019)
- [Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph](https://mlsanity.com/paper/2004.06842/) (2020)
- [Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity](https://mlsanity.com/paper/1603.04225/) (2016)
- [EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces](https://mlsanity.com/paper/2010.12370/) (2020)
- [Learning to Recommend Items to Wikidata Editors](https://mlsanity.com/paper/2107.06423/) (2021)
- [EventKG+TL: Creating Cross-Lingual Timelines from an Event-Centric Knowledge Graph](https://mlsanity.com/paper/1805.01359/) (2018)
- [Network-based recommendation algorithms: A review](https://mlsanity.com/paper/1511.06252/) (2015)
- [Towards Better Understanding Researcher Strategies in Cross-Lingual Event Analytics](https://mlsanity.com/paper/1809.08084/) (2018)
- [MultiWiki: Interlingual Text Passage Alignment in Wikipedia](https://mlsanity.com/paper/1905.08675/) (2019)

## Abstract (arXiv)

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the C'esar Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events.