Highlights §
- search and recom- mendation are foundational infrastructures to satisfying users’ in- formation needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items (View Highlight)
- we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses (View Highlight)
- Nowadays, two information access modes — search and recommendation — serve as foundational infrastructures for information delivery. The objective of search is to retrieve a list of documents (e.g., Web documents, Twitter posts, and answers) given the user’s explicit query [57]. By contrast, recommendation systems aim to recommend the items (e.g., E-commerce products, micro-videos, and news) by implic- itly inferring user interest from the user’s profile and historical interactions [1] (View Highlight)
- Search and recommendation are the two sides ofthe same coin [6]. Search is users’ active information retrieval with explicit queries while recommendation is passive information filtering for users. (View Highlight)
- . Search can be formulated as a matching between queries and documents, and recommendation can be considered a match- ing between users and items. (View Highlight)
- Machine learning-based search and recommendation. (View Highlight)
- learn a match- ing function using machine learning techniques (e.g., learning to ranks [65, 89] and Matrix Factorization [61]) to estimate the relevance scores on query-document pairs or user-item pairs. (View Highlight)
- Deep learning-based search and recommendation (View Highlight)
- m. This paradigm leverages the powerful represen- tation ability of deep learning-based methods to encode inputs (i.e., queries, documents, users, and items) into dense vectors in a latent space [55] and learn the non-linear matching functions. (View Highlight)
- Generative search and recommendation. (View Highlight)
- enerative search (retrieval)1 and recommendation. (View Highlight)
- Machine learning-based search. (View Highlight)
- he series of “learning to rank” algorithms [65, 89] were also proposed to develop effective rank losses for machine learning based search methods: the pointwise ap- proaches [19, 102, 119] transform ranking into regression or classifi- cation on single documents; the pairwise approaches [10, 107, 135] regard the ranking into classification on the pairs of documents; the listwise approaches [12, 30, 106, 146, 155] aim to model the ranking problem in a straightforward fashion and overcome the drawbacks of the aforementioned two approaches by tackling the ranking problem directly. (View Highlight)
- Machine learning-based recommendation. (View Highlight)