Combining textual web search with spatial, temporal and social aspects of the web

Over the last few years, Web has changed significantly. Emergence of Web 2.0 has enabled people to interact with web documents in new ways not possible before. It is now a common practice to geo-tag or time-tag web documents, or to integrate web documents with popular social networks. With these new changes and the abundant usage of spatial, temporal and social information in web documents and search queries, the necessity of integration of such non-textual aspects of the web to the regular textual web search has grown rapidly over the past few years. To integrate each of those non-textual dimensions to the textual web search and to enable spatial-textual, temporal-textual and social-textual web search, in this dissertation we propose a set of new relevance models, index structures and algorithms specifically designed for adding each non-textual dimension (spatial, temporal and social) to the current state of (textual) web search. First, we propose a new ranking model and a hybrid index structure called Spatial-Keyword Inverted File to handle location-based ranking and indexing of web documents in an integrated/efficient manner. Second, we propose a new indexing and ranking framework for temporal-textual retrieval. The framework leverages the classical vector space model and provides a complete scheme for indexing, query processing and ranking of the temporal-textual queries. Finally, we show how to personalizes the search results based on users' social actions. We propose a new relevance model called PerSocial relevance model utilizing three levels of social signals to improve the web search. Furthermore, we develop several approaches to Integrate PerSocial relevance model into the textual web search process.