A probability-based unified framework for semantic search and recommendation
The objective of search and recommendation is to provide users with documents that are relevant to their needs. Keyword-based search and recommendation approaches suffer from sparsity and semantic ambiguity problems because they correlate users' needs with documents only via keywords. Thus, for a given query, some documents that are semantically relevant to a user's needs are not provided if they do not include specific keywords. To address this, some search approaches have used the authority of documents, which is commonly represented using hyperlinks within documents. However, if there are no hyperlinks, it is difficult to exploit the authority for ranking documents. As the links of documents are determined by their owners, the authority derived from links does not consider users' current needs. In order to resolve these problems, we propose a unified framework for semantic search and recommendation to enrich the semantics of users' needs and documents with their corresponding concepts and to use personalized authority derived from recommendation approaches. The proposed approach makes it possible to retrieve documents with a high degree of semantic relevance as well as high authority. Through extensive experiments, we show that our approach outperforms conventional search and recommendation approaches.
semantic search recommendation personalization collaborative filtering information retrieval probability web search