Deep Learning for Matching in Search and Recommendation

By Jun Xu, Xiangnan He, Hang Li. 2018

122 citations recorded by the source corpus.

Matching is the key problem in both search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as "learning to match''. In recent years, deep learning has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep learning for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine learning techniques are utilized to address the matching problem in search and recommendation. Lastly, we elaborate how deep learning can be effectively used to solve the matching problems in both tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fuli Feng
2. Xiangnan He
3. Jiawei Chen
4. Tianxin Wei
5. Jinfeng Yi
6. Chufeng Shi
7. Ziwei Wu
8. Xiang Wang

In Computer Vision and Pattern Recognition

1. Fuli Feng
2. Xiangnan He
3. Jiawei Chen
4. Tianxin Wei
5. Jinfeng Yi
6. Chufeng Shi
7. Ziwei Wu
8. Tat-seng Chua

In Information Systems

1. Fuli Feng
2. Xiangnan He
3. Jiawei Chen
4. Tianxin Wei
5. Jinfeng Yi
6. Chufeng Shi
7. Ziwei Wu
8. Xiang Wang

Citations in the corpus

• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “The key objective of embedding-based recommendation model is to capture relation between of user and item in the latent space [41], therefore it is critical to explicitly build the relation between transferred item embeddings and local user embedding in our problem.”
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2020, background): “However, existing recommender models largely focus on the user-item matching factor [22, 53] (Figure 2(a)), ignoring how the item popularity affects the interaction probability (Figure 2(b)).”
• A Survey of Model Architectures in Information Retrieval (2025, unknown): “C and detailed surveys (Onal et al., 2018; Mitra et al., 2018; Xu et al., 2018) independently encode queries and documents into a latent vector space, as illustrated in Fig.”