Search Personalization with Embeddings

Abstract

Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user's topical interests. In this paper, we propose a new embedding approach to learning user profiles, where users are embedded on a topical interest space. We then directly utilize the user profiles for search personalization. Experiments on query logs from a major commercial web search engine demonstrate that our embedding approach improves the performance of the search engine and also achieves better search performance than other strong baselines.

In Proceedings of the 39th European Conference on Information Retrieval, ECIR 2017, to appear

References in corpus (3)

• STransE: a novel embedding model of entities and relationships in knowledge bases
• Neighborhood Mixture Model for Knowledge Base Completion
• Personalised Query Suggestion for Intranet Search with Temporal User Profiling

Cited by in corpus (8)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• Personalised Query Suggestion for Intranet Search with Temporal User Profiling
• Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
• Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
• A Capsule Network-based Embedding Model for Search Personalization
• PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
• Group based Personalized Search by Integrating Search Behaviour and Friend Network
• Personalizing Search Results Using Hierarchical RNN with Query-aware Attention