Semantic Models for the First-Stage Retrieval: A Comprehensive Review - dl.acm.org/doi/abs/10.1145/348

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation - dl.acm.org/doi/abs/10.1145/350

Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling - dl.acm.org/doi/abs/10.1145/350

A Re-classification of Information Seeking Tasks and Their Computational Solutions - dl.acm.org/doi/abs/10.1145/349

Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs - dl.acm.org/doi/abs/10.1145/348

MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction - dl.acm.org/doi/abs/10.1145/350

Dynamic Graph Reasoning for Conversational Open-Domain Question Answering - dl.acm.org/doi/abs/10.1145/349

“What Can I Cook with these Ingredients?” - Understanding Cooking-Related Information Needs in Conversational Search - dl.acm.org/doi/abs/10.1145/349

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning - dl.acm.org/doi/abs/10.1145/349

Understanding the “Pathway” Towards a Searcher’s Learning Objective - dl.acm.org/doi/abs/10.1145/349

Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs - dl.acm.org/doi/abs/10.1145/349

Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents? - dl.acm.org/doi/abs/10.1145/349

Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach - dl.acm.org/doi/abs/10.1145/349

Grounded Task Prioritization with Context-Aware Sequential Ranking - dl.acm.org/doi/abs/10.1145/348

Learning Text-image Joint Embedding for Efficient Cross-modal Retrieval with Deep Feature Engineering - dl.acm.org/doi/abs/10.1145/349

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Mastodon

The "unofficial" Information Retrieval Mastodon Instance.

Goal: Make idf.social a viable and valuable social space for anyone working in Information Retrieval and related scientific research.

Everyone welcome but expect some level of geekiness on the instance and federated timelines.