Modeling Temporal Evidence from External Collections

Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.Comment: To appear in WSDM 201

Task-based user profiling for query refinement (toque)

The information needs of search engine users vary in complexity. Some simple needs can be satisfied by using a single query, while complicated ones require a series of queries spanning a period of time. A search task, consisting of a sequence of search queries serving the same information need, can be treated as an atomic unit for modeling user’s search preferences and has been applied in improving the accuracy of search results. However, existing studies on user search tasks mainly focus on applying user’s interests in re-ranking search results. Only few studies have examined the effects of utilizing search tasks to assist users in obtaining effective queries. Moreover, fewer existing studies have examined the dynamic characteristics of user’s search interests within a search task. Furthermore, even fewer studies have examined approaches to selective personalization for candidate refined queries that are expected to benefit from its application. This study proposes a framework of modeling user’s task-based dynamic search interests to address these issues and makes the following contributions. First, task identification: a cross-session based method is proposed to discover tasks by modeling the best-link structure of queries, based on the commonly shared clicked results. A graph-based representation method is introduced to improve the effectiveness of link prediction in a query sequence. Second, dynamic task-level search interest representation: a four-tuple user profiling model is introduced to represent long- and short-term user interests extracted from search tasks and sessions. It models user’s interests at the task level to re-rank candidate queries through modules of task identification and update. Third, selective personalization: a two-step personalization algorithm is proposed to improve the rankings of candidate queries for query refinement by assessing the task dependency via exploiting a latent task space. Experimental results show that the proposed TOQUE framework contributes to an increased precision of candidate queries and thus shortened search sessions

Entity Query Feature Expansion Using Knowledge Base Links

Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity-based feature expansion results in significant improvements in retrieval effectiveness over state-of-the-art text expansion approaches

A New Approach of Clustering Feedback Sessions for Inferring User Search Goals

Internet information is growing every day exponentially. In order to find out the exact required information from this web search engines has become absolutely necessary tool for the web users. It has also become more difficult to provide user the required information. When Different users provide an ambiguous query to a search engine, they might be having different search goals. Therefore, it is required to find and analyze user search goals to improve the performance of a search engine and user experience. By representing the results in cluster we find out different user search goals for a query. It has advantages in improving search engine relevance and user experience. It extends the delivery and quality of internet information services to the end user. It also improves performance of Web server system. Query classification, search result reorganization and session boundary detection are the approaches attempt to find out user search goals. But the mentioned approaches has many limitations. A new approach has been implemented that overcomes the limitations and analyze, discover user search goals using feedback sessions. This approach first takes the user search query. For each single result of the search query pseudo-documents are generated. Using K-means++ clustering algorithm, these pseudo-documents are clustered. Each cluster can be considered as one user search goal. Finally in restructured result is given to the user where each URL is categorized into a cluster centered by the inferred search goals. Then depending upon user click through, results are restructured and represented to the user in order to satisfy the information need. DOI: 10.17762/ijritcc2321-8169.15071