C4.5: Programs for Machine Learning (書評)

The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user’s original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed. Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)?

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A Survey of Automatic Query Expansion in Information Retrieval

Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.

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Personalized news recommendation based on click behavior

In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. We show that on several well-known social media sites, we can easily and accurately recover the information of private-profile users. To the best of our knowledge, this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles.

To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.

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Persona: an online social network with user-defined privacy

There has been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.

/pdf/automatic-identification-of-user-goals-in-web-search-5d8uaxc0ss.pdf

Automatic identification of user goals in Web search

Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we study the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network. Our experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships. Further, even in a society where most people hide their attributes, it is still possible to infer privacy information.

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Inferring privacy information from social networks

In retrieving medical free text, users are often interested in answers pertinent to certain scenarios that correspond to common tasks performed in medical practice, e.g., treatment or diagnosis of a disease. A major challenge in handling such queries is that scenario terms in the query (e.g., treatment) are often too general to match specialized terms in relevant documents (e.g., chemotherapy). In this paper, we propose a knowledge-based query expansion method that exploits the UMLS knowledge source to append the original query with additional terms that are specifically relevant to the query's scenario(s). We compared the proposed method with traditional statistical expansion that expands terms which are statistically correlated but not necessarily scenario specific. Our study on two standard testbeds shows that the knowledge-based method, by providing scenario-specific expansion, yields notable improvements over the statistical method in terms of average precision-recall. On the OHSUMED testbed, for example, the improvement is more than 5% averaging over all scenario-specific queries studied and about 10% for queries that mention certain scenarios, such as treatment of a disease and differential diagnosis of a symptom/disease.

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Knowledge-based query expansion to support scenario-specific retrieval of medical free text

Although search engines are playing an increasingly important role in users’ Web access, our understanding is still limited regarding the magnitude of search-engine influence. For example, how many times do people start browsing the Web from a search engine? How much percentage of Web traffic is incurred as a result of search? To what extent does a search engine like Google extend the scope of Websites that users can reach? To study these issues, in this paper we analyze a real Web access trace collected over a period of two and half months from the UCLA Computer Science Department. Our study indicates that search engines influence about 13.6% of the users’ Web traffic directly and indirectly. In addition, our study provides realistic estimates for certain key parameters used for Web modelling.

/pdf/analysis-of-user-web-traffic-with-a-focus-on-search-3zjnziq6zs.pdf

Analysis of User Web Traffic with A Focus on Search Activities.

In retrieving medical free text, users are often interested in answers relevant to certain scenarios, scenarios that correspond to common tasks in medical practice, e.g., "treatment" or "diagnosis" of a disease. Consequently, the queries they pose are often scenario-specific, e.g., "lung cancer, treatment." A fundamental challenge in handling such queries is that scenario terms in the query (e.g. "treatment") are too general to match specialized terms in relevant documents (e.g. "lung excision"). In this paper we propose a knowledge-based query expansion method that exploits the UMLS knowledge source to append the original query with additional terms that are specifically relevant to the query's scenario(s). We compare the proposed method with statistical expansion that only explores statistical term correlation and expands terms that are not necessarily scenario specific. Our study on the OHSUMED testbed shows that the knowledge-based method which results in scenario-specific expansion is able to improve more than 5% over the statistical method on average, and about 10% for queries that mention certain scenarios, such as "treatment of a disease" and "differential diagnosis of a symptom/disease."

/pdf/knowledge-based-query-expansion-to-support-scenario-specific-y8a8sqgza7.pdf

Zhenyu Liu

Papers

Automatic identification of user goals in Web search

Inferring privacy information from social networks

Knowledge-based query expansion to support scenario-specific retrieval of medical free text

Analysis of User Web Traffic with A Focus on Search Activities.

Knowledge-based query expansion to support scenario-specific retrieval of medical free text