Search personalization through query and page topical analysis

Abstract

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user's topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user's current query and the query-matching pages, in order to identify the user's current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

“…Therefore, this system has a higher degree of granularity over the system by Speretta and Gauch, since it takes into account more refined user preferences. An even more sophisticated classification is proposed in Stamou and Ntoulas (2009), where a topical ontology is created using ODP categories in conjunction with the Wordnet and SUMO ontologies. Users' past queries are mapped to categories using several methods, including ontology traversal.…”

Building Rich User Search Queries Profiles

“…Personalization is a well researched area that aims at better addressing search intents of users for tasks such as document (re)ranking or document retrieval [1], [4], [9], [11], [12], [15], [17], [19], [20]. The value of personalizing web search results has widely been studied [4], [12], [19]: Teevan et al [19] analyze query intents of users and discover that there are noticeable variations in search intents for the same query and in the interpretation of these intents.…”

“…These works primarily study users' intents over time for better document ranking [1], [4], [9], [11], [12], [15], [17], [19], [20] or they research query suggestion utilizing document click through [3], [8], [13], [16]. Building deep profiles from terse queries poses many interesting challenges: (1) we have a cold-start problem [17], [18], [20], i.e., information about the user needs to be gathered first before queries or results can be personalized; (2) a substantial fraction of queries does not yield to personalization (navigational queries like 'facebook', 'bank of america', etc.) or purely numeric queries; (3) search queries are terse and often ambiguous.…”

Ontological Approach for Effective Generation of Concept Based User Profiles to Personalize Search Results

“…Here, p(k1, k2) represents the probability of keyword pair (k1, k2) and Nf (k) is a normalization feature specifies the amount of keyword minds that exists in keyword k (Stamou and Ntoulas, 2009). Associations initiate relations between topics by generating associations.…”

“…User preferences give major improvements of the search results quality. User interests may be identified by watching user's surfing activities over period (Stamou and Ntoulas, 2009).…”