Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation

This paper discusses a longitudinal user evaluation of Prospector, a personalized Internet meta-search engine capable of personalized re-ranking of search results. Twenty-one participants used Prospector as their primary search engine for 12 days, agreed to have their interaction with the system logged, and completed three questionnaires. The data logs show that the personalization provided by Prospector is successful: participants preferred re-ranked results that appeared higher up. However, the questionnaire results indicated that people would prefer to use Google instead (their search engine of choice). Users would, nevertheless, consider employing a personalized search engine to perform searches with terms that require disambiguation and/or contextualization. We conclude the paper with a discussion on the merit of combining system- and user-centered evaluation for the case of personalized systems

Domain-specific queries and Web search personalization: some investigations

Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their preferences. This paper moves from early results on quantification of personalization over Google search query results. Inspired by previous works, we have carried out some experiments consisting of search queries performed by a battery of Google accounts with differently prepared profiles. Matching query results, we quantify the level of personalization, according to topics of the queries and the profile of the accounts. This work reports initial results and it is a first step a for more extensive investigation to measure Web search personalization.Comment: In Proceedings WWV 2015, arXiv:1508.0338

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment

Privacy Protection in Web Search

This paper presents web search has demonstrated in improving the quality of various search services on the internet, user reluctance to disclose the private information during search has become major barrier for the wide proliferation of password. Protection in password authentication model user preferences as hierarchical user profiles, a password framework know as user profile search that can adaptively generalize profile by search query while respecting user specified privacy requirements. Our work provides utility of personalization and the privacy risk of exposing the generalized profile using Greedy algorithm is a method for deciding whether personalizing a query is efficient

The use of public Web portals by undergraduate students

The study explored how and why 144 randomly selected undergraduates’ from a large university in the U.S. use public Web portals such as Yahoo! or MSN. Demographic and use variables regarding information about particular portal features were collected with a standardized questionnaire including open-ended and closed questions from June to October 2002. All but two respondents were users of public Web portals. A second phase consisted of eight tape-recorded focus groups with 42 participants. In addition, 39 individual follow-up interviews were conducted. The questionnaire data were analyzed using Chi-Square (α = 0.05) to test hypotheses for statistical significance. The focus groups, interviews, and open-ended questions were content analyzed and identified a variety of problems that undergraduates faced using portals. The study provides empirical data about undergraduates’ characteristics, e.g., gender, major, classification, GPA, computer and network experience, times of portal use, and use of personalization in relation to the use of public Web portals and the possession of personal home pages. The study sheds light on why and how undergraduates seek information on public Web portals, what they do on these sites, and reasons for using and not using portals and particular portal features. According to the introduced Popularity Index of Public Web Portals, Yahoo! and MSN were the most popular portals, while searches, e-mail, world and national news were the most popular features for undergraduates using these sites. About 50% of the participants used personalization. Personalizers used portals to a greater extent and were satisfied. Lack of personalization and other factors were a reason for limited use of portals. Demographic variables such as gender, age, and major did not show statistical significance for the use of public Web portals, while use variables such as Internet access at home, frequency of portal use, and the possession of a personal home page showed significant relationships. Frequent redesign, privacy concerns, and unsolicited advertising were among reasons for limited use. The study’s results contribute to a better understanding of undergraduates’ information needs and behavior on public Web portals. The findings have implications for libraries, universities, governments, Web content developers, and marketers.Informatio