Personalized Query Suggestion Research Articles

Personalized query suggestion diversification in information retrieval

Query suggestions help users refine their queries after they input an initial query. Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based, developing models that either focus on adapting to a specific user (personalization) or on diversifying query aspects in order to maximize the probability of the user being satisfied (diversification). We consider the task of generating query suggestions that are both personalized and diversified. We propose a personalized query suggestion diversification (PQSD) model, where a user’s long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user’s search context in their current session. Query aspects are identified through clicked documents based on the open directory project (ODP) with a latent dirichlet allocation (LDA) topic model. We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online (AOL) query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification. The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries, especially when more query suggestions are returned in the list.

The Role of the User's Browsing and Query History for Improving MPC-generated Query Suggestions

In this paper we use the user's recent web browsing history in order to provide better query suggestions in an information retrieval system. We have built a Chrome browser plugin that collects each web page visited by a user and submits it to our query suggestion server for indexing, thus building a personal history profile for each user. We then analyze if future queries submitted by a user to the search engine can be predicted from web pages visited by that user in the past (i.e. his recent browsing history) or from queries submitted by that user in the past (i.e. his recent query history). The contribution of this paper is a method of using this personal history profile for reordering the query suggestions offered by Google when the user searches information on Google, moving query suggestions more relevant to the user's information need to the front positions in the Google provided query suggestions list. We have collected browsing history log data for over 4 months from several users who installed our Chrome plugin on their local computers and then we performed an offline evaluation test that shows that our personalized query suggestion system improves the MRR (i.e. Mean Reciprocal Rank) score of Google query suggestions by approximately 0.04 (i.e. improves Google's MRR score by 12 percents).

Personalized query suggestion based on user behavior

Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

Evaluation and analysis of term scoring methods for term extraction

We evaluate five term scoring methods for automatic term extraction on four different types of text collections: personal document collections, news articles, scientific articles and medical discharge summaries. Each collection has its own use case: author profiling, boolean query term suggestion, personalized query suggestion and patient query expansion. The methods for term scoring that have been proposed in the literature were designed with a specific goal in mind. However, it is as yet unclear how these methods perform on collections with characteristics different than what they were designed for, and which method is the most suitable for a given (new) collection. In a series of experiments, we evaluate, compare and analyse the output of six term scoring methods for the collections at hand. We found that the most important factors in the success of a term scoring method are the size of the collection and the importance of multi-word terms in the domain. Larger collections lead to better terms; all methods are hindered by small collection sizes (below 1000 words). The most flexible method for the extraction of single-word and multi-word terms is pointwise Kullback---Leibler divergence for informativeness and phraseness. Overall, we have shown that extracting relevant terms using unsupervised term scoring methods is possible in diverse use cases, and that the methods are applicable in more contexts than their original design purpose.

Search Query Expansion using Genetic Algorithm?based Clustering

The World Wide Web has become a resource pool for people seeking information. Current Web search engines try to deliver relevant information to users, but due to both exponential growth of information and imprecise queries, search engines cannot meet users’ information requirements. Users have to reframe queries until they get the desired information. To improve users’ search experience, some search engines provide query suggestions that are semantically related to a particular query. These systems provide the same suggestions to the same queries without considering the personal interest of the user. This paper presents an approach to provide personalized query suggestions based on a genetic algorithm?based clustering technique. This improves retrieval effectiveness and relevancy by expanding the query with additional words. Unlike existing methods, this technique provides personalized query suggestions for each individual user according to that user’s conceptual needs. The main objective of this work is to improve retrieval of information by expanding the user’s query based on the user’s domain of interest.

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of 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 two-phase 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.