Personalized Query Suggestion With Diversity Awareness

Abstract

Query suggestion is an important functionality provided by the search engine to facilitate information seeking of the users. Existing query suggestion methods usually focus on recommending queries that are the most relevant to the input query. However, such relevance-oriented strategy cannot effectively handle query uncertainty, a common scenario that the input query can be interpreted as multiple different meanings. To alleviate this problem, the concepts of diversification and personalization have been individually introduced to query suggestion systems. These two concepts are often seen as incompatible alternatives, because diversification considers multiple aspects of the input query to maximize the probability that some query aspect is relevant to the user while personalization aims to adapt the suggestions to a specific aspect that aligns with the preference of a specific user. In this paper, we refute this antagonistic view and propose a new query suggestion paradigm, Personalized Query Suggestion With Diversity Awareness (PQS-DA) to effectively combine diversification and personalization into one unified framework. In PQS-DA, the suggested queries are effectively diversified to cover different potential facets of the input query while the ranking of suggested queries are personalized to ensure that the top ones are those that align with a user's personal preference. We evaluate PQS-DA on a real-life search engine query log against several state-of-the-art methods with respect to a variety of metrics. The experimental results verify our hypothesis that diversification and personalization can be effectively integrated and they are able to enhance each other within the PQS-DA framework, which significantly outperforms several strong baselines with respect to a series of metrics. I. INTRODUCTIONQuery suggestion is an important functionality provided by contemporary web search engines to help users formulate more effective search queries [1]. Most of the existing query suggestion methods [2][3][4][1] [5] belong to the category of relevance-oriented query suggestion, which focuses on maximizing the overall relevance of the suggested queries in response to an input query. However, since search queries are typically short and ambiguous [6], the simplistic relevanceoriented methods usually fail in the face of query uncertainty, which widely exists in the scenario of general web search.Consider the following example of query uncertainty: When the search query "sun" is submitted to the search engine, the underlying information need can be related to at least one of the three facets: the star of the solar system, the computer manufacturer named Sun Microsystems or a newspaper in the United Kingdom. In this case, the relevance-oriented approaches usually generate suggestions that cover a few

Query suggestion with diversification and personalization

“…This paper significantly enriches the version of [21], and the major updates are as follows: (1) We propose a new strategy to identify the first query suggestion candidate and the new strategy has much higher precision. (2) We propose a novel technique to identify the topically significant query words and URLs.…”

As-You-Type Social Aware Search

“…In [Fafalios and Tzitzikas 2015], the authors discuss in depth various systems choices involving index partitioning or caching, for query auto-completion under typo-tolerant and wordorder tolerant assumptions. Query suggestion goes one step further by proposing alternative queries, which are not necessarily completions of the input one (see for instance [Vahabi et al 2013;Jiang et al 2014]). In comparison, our work does not focus on queries as first-class citizens, but on instant results to incomplete queries.…”

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

“…Deng et al [5] make diversity recommendation through user preferences and dynamic interests. Jiang et al [6] propose a search engine query recommendation method that comprehensively considers personalization and diversification. Cheng et al [7] propose a recommendation method that takes into account product accuracy and diversity through supervised learning.…”