Praveen Chandar

Probabilistic models of ranking novel documents for faceted topic retrieval

Traditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval models need to capture dependencies between documents. Such tasks require alternative evaluation and optimization methods that operate on different types of relevance judgments. We define faceted topic retrieval as a particular novelty-driven task with the goal of finding a set of documents that cover the different facets of an information need. A faceted topic retrieval system must be able to cover as many facets as possible with the smallest number of documents. We introduce two novel models for faceted topic retrieval, one based on pruning a set of retrieved documents and one based on retrieving sets of documents through direct optimization of evaluation measures. We compare the performance of our models to MMR and the probabilistic model due to Zhai et al. on a set of 60 topics annotated with facets, showing that our models are competitive.

Alternative assessor disagreement and retrieval depth

Assessors are well known to disagree frequently on the relevance of documents to a topic, but the factors leading to assessor disagreement are still poorly understood. In this paper, we examine the relationship between the rank at which a document is returned by a set of retrieval systems and the likelihood that a second assessor will disagree with the relevance assessment of the initial assessor, and find that there is a strong and consistent correlation between the two. We adopt a metarank method of summarizing a documents rank across multiple runs, and propose a logistic regression predictive model of second assessor disagreement given metarank and initially-assessed relevance. The consistency of the model parameters across different topics, assessor pairs, and collections is considered. The model gives comparatively accurate predictions of absolute system scores, but less consistent predictions of relative scores than a simpler rank-insensitive model. We demonstrate that the logistic regression model is robust to using sampled, rather than exhaustive, dual assessment. We demonstrate the use of the sampled predictive model to incorporate assessor disagreement into tests of statistical significance.

Using preference judgments for novel document retrieval

There has been considerable interest in incorporating diversity in search results to account for redundancy and the space of possible user needs. Most work on this problem is based on subtopics: diversity rankers score documents against a set of hypothesized subtopics, and diversity rankings are evaluated by assigning a value to each ranked document based on the number of novel (and redundant) subtopics it is relevant to. This can be seen as modeling a user who is always interested in seeing more novel subtopics, with progressively decreasing interest in seeing the same subtopic multiple times. We put this model to test: if it is correct, then users, when given a choice, should prefer to see a document that has more value to the evaluation. We formulate some specific hypotheses from this model and test them with actual users in a novel preference-based design in which users express a preference for document A or document B given document C. We argue that while the user study shows the subtopic model is good, there are many other factors apart from novelty and redundancy that may be influencing user preferences. From this, we introduce a new framework to construct an ideal diversity ranking using only preference judgments, with no explicit subtopic judgments whatsoever.

Preference based evaluation measures for novelty and diversity

Novel and diverse document ranking is an effective strategy that involves reducing redundancy in a ranked list to maximize the amount of novel and relevant information available to users. Evaluation for novelty and diversity typically involves an assessor judging each document for relevance against a set of pre-identified subtopics, which may be disambiguations of the query, facets of an information need, or nuggets of information. Alternately, when expressing a \emph{preference} for document A or document B, users may implicitly take subtopics into account, but may also take into account other factors such as recency, readability, length, and so on, each of which may have more or less importance depending on user. A \emph{user profile} contains information about the extent to which each factor, including subtopic relevance, plays a role in the users preference for one document over another. A preference-based evaluation can then take this user profile information into account to better model utility to the space of users. In this work, we propose an evaluation framework that not only can consider implicit factors but also handles differences in user preference due to varying underlying information need. Our proposed framework is based on the idea that a user scanning a ranked list from top to bottom and stopping at rank

Document features predicting assessor disagreement

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Diversification of search results using webgraphs

gains some utility from every document that is relevant their information need. Thus, we model the expected utility of a ranked list by estimating the utility of a document at a given rank using preference judgments and define evaluation measures based on the same. We validate our framework by comparing it to existing measures such as

Offline Comparative Evaluation with Incremental, Minimally-Invasive Online Feedback

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Estimating Clickthrough Bias in the Cascade Model

-nDCG, ERR-IA, and subtopic recall that require explicit subtopic judgments We show that our proposed measures correlate well with existing measures while having the potential to capture various other factors when real data is used. We also show that the proposed measures can easily handle relevance assessments against multiple user profiles, and that they are robust to noisy and incomplete judgments.

Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval

The notion of relevance differs between assessors, thus giving rise to assessor disagreement. Although assessor disagreement has been frequently observed, the factors leading to disagreement are still an open problem. In this paper we study the relationship between assessor disagreement and various topic independent factors such as readability and cohesiveness. We build a logistic model using reading level and other simple document features to predict assessor disagreement and rank documents by decreasing probability of disagreement. We compare the predictive power of these document-level features with that of a meta-search feature that aggregates a documents ranking across multiple retrieval runs. Our features are shown to be on a par with the meta-search feature, without requiring a large and diverse set of retrieval runs to calculate. Surprisingly, however, we find that the reading level features are negatively correlated with disagreement, suggesting that they are detecting some other aspect of document content.

Udel @ NTCIR-11 IMine Track.

A set of words is often insufficient to express a users information need. In order to account for various information needs associated with a query, diversification seems to be a reasonable strategy. By diversifying the result set, we increase the probability of results being relevant to the users information needs when the given query is ambiguous. A diverse result set must contain a set of documents that cover various subtopics for a given query. We propose a graph based method which exploits the link structure of the web to return a ranked list that provides complete coverage for a query. Our method not only provides diversity to the results set, but also avoids excessive redundancy. Moreover, the probability of relevance of a document is conditioned on the documents that appear before it in the result list. We show the effectiveness of our method by comparing it with a query-likelihood model as the baseline.