Abstract-A major problem of current Web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new twophase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.

Abstract-User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e. positive preferences), but not the objects that users dislike (i.e. negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.

Microblogging platforms, such as Twitter, have already played an important role in recent cultural, social and political events. Discovering latent topics from social streams is therefore important for many downstream applications, such as clustering, classification or recommendation. However, traditional topic models that rely on the bag-of-words assumption are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, microblog content is often influenced by external information sources, such as Web documents linked from Twitter posts, and often focuses on specific entities, such as people or organizations. These external sources provide useful semantics to understand microblogs and we generally refer to these semantics as auxiliary semantics. In this article, we address the mentioned issues and propose a unified framework for Multifaceted Topic Modeling from Twitter streams. We first extract social semantics from Twitter by modeling the social chatter associated with hashtags. We further extract terms and named entities from linked Web documents to serve as auxiliary semantics during topic modeling. The Multifaceted Topic Model (MfTM) is then proposed to jointly model latent semantics among the social terms from Twitter, auxiliary terms from the linked Web documents and named entities. Moreover, we capture the temporal characteristics of each topic. An efficient online inference method for MfTM is developed, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We evaluate each aspect of our framework and show its utility in the context of tweet clustering.