Adapting to the user's internet search strategy on small devices (original) (raw)

Adapting to the User’s Internet Search Strategy

Lecture Notes in Computer Science, 2003

World Wide Web search engines typically return thousands of results to the users. To avoid users browsing through the whole list of results, search engines use ranking algorithms to order the list according to predefined criteria. In this paper, we present Toogle, a front-end to the Google search engine for mobile phones offering web browsing. For a given search query, Toogle first ranks results using Google's algorithm and, as the user browses through the result list, uses machine learning techniques to infer a model of her search goal and to adapt accordingly the order in which yet-unseen results are presented. We report preliminary experimental results that show the effectiveness of this approach.

PERSONALIZED MOBILE SEARCH ENGINE

In today‟s world we need everything in our hands and fast. In this paper we propose as system which helps the user to search the data he wants on the World Wide Web using his smart phone and based on the user‟s current location the search results would vary as the search would be based on the current location of the user which would be mapped using the GPS. The user preferences are organized in an ontology-based, multi-facet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results.

Context-Based Personalization for Mobile Web Search

2008

User experience while searching for web pages on the move can be far from satisfactory due to the inherent limitations of the input modes available in mobile devices. On the other hand, end-users can benefit from the availability of contextaware information anywhere, anytime. To overcome the usability problem and exploit context information at the same time, we propose a thesaurus-based semantic context-aware autocompletion mechanism. Our system can help the user in completing the desired query terms avoiding manual typing. In addition we are capable of filtering out non-relevant query terms for the Context in which the search process is conducted. Our context-aware proposal is based on a model which represents formally all the information about the user circumstances, the access mechanism (device and web browser) and the surrounding environment. Our evaluation reveals that users can find new relevant context-aware results with less effort.

A Search Engine for Personal Mobile

2014

A personalized mobile search engine (PMSE) that captures the users’ preferences in the form of concepts by mining their clickthrough data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users’ locations (positioned by GPS) are used to supplement the location concepts in PMSE. The user preferences are organized in an ontology-based, multifacet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevance’s to the user’s need, four entropies are introduced to balance the weights between the content and location facets. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction, training, and reranking are performed at the PMSE server. Moreover, we address t...

Towards more intelligent mobile search

2005

Abstract As the mobile Internet continues to grow there is an increasing need to provide users with effective search facilities. In this paper we argue that the standard Web search approach of providing snippet text alongside each result is not appropriate given the interface limitations of mobile devices. Instead we evaluate an alternative approach involving the use of related queries in place of snippet text for result gisting.

Optimized Mobile Search Engine Using Click-Through Data

International Conference on Information Engineering, Management and Security 2014, 2014

Data mining is a system employing for more computer learning technique to automatically analyse and extracting knowledge from data stored in the database. The goal of data mining is to extract hidden predictive information from database. This paper make use of data mining concept for collecting user’s multiple preference from click through data. we propose a personalized mobile search engine (PMSE) that captures the users’ preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users’ locations (positioned by GPS) are used to represent the location concepts in PMSE. The user preferences are organized in an ontology-based, multi facet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevance to the user’s need. based on the client-server model, we also present a detailed architecture and design for implementation of PMSE. In our design, the client collects and stores locally the click through data to protect privacy, whereas heavy tasks such as concept extraction, training, and reranking are performed at the PMSE server. Moreover, we prototype PMSE on the Google Android platform.

Uncovering User’s Search Patterns to Personalise Web Search

2018

In today’s world, search engines have become a very convenient method of searching and retrieving information. But this increasing use of search engines goes hand in hand with the everincreasing data available on the internet. With such large number of websites available, it is essential to have these websites sorted in decreasing order of their relevance to the user’s query for effective operation and retrieval of data. This paper explores various domains related to Computer Science and proposes a framework that seems the best fix to this problem. We have proposed a new system to provide personalized web search according to the user’s internet surfing patterns. The system extracts the user’s history and scrapes the web pages’ content (title, keywords, headings, sub-headings, meta tags). These documents are then clustered using Word2Vec model and Latent Semantic Indexing to give better results. User’s search query is mapped to the profile and an appropriate cluster is selected. The ...

Who, what, where & when: a new approach to mobile search

2008

ABSTRACT Mobile devices and the mobile Internet represent an extremely challenging search environment. Limited screenspace, restricted text-input and interactivity, and impatient users all conspire to exacerbate the shortcomings of modern Web search. Recently researchers have proposed that typically vague search queries be augmented by context information, as a way to help search engines to retrieve more relevant information.

MobEx: A System for Exploratory Search on the Mobile Web

We present MobEx, a mobile touchable application for exploratory search on the mobile web. The system has been implemented for operation on a tablet computer, i.e. an Apple iPad, and on a mobile device, i.e. Apple iPhone or iPod touch. Starting from a topic issued by the user the system collects web snippets that have been determined by a standard search engine in a first step and extracts associated topics to the initial query in an unsupervised way on-demand and highly performant. This process is recursive in priciple as it furthermore determines other topics associated to the newly found ones and so forth. As a result MobEx creates a dense web of associated topics that is presented to the user as an interactive topic graph. We consider the extraction of topics as a specific empirical collocation extraction task where collocations are extracted between chunks combined with the cluster descriptions of an online clustering algorithm. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. These syntactically-oriented chunk pairs are then semantically ranked and filtered using the cluster descriptions created by a Singular Value Decomposition (SVD) approach. An initial user evaluation shows that this system is especially helpful for finding new interesting information on topics about which the user has only a vague idea or even no idea at all.