Omid Madani

Generating query substitutions

We introduce the notion of query substitution, that is, generating a new query to replace a users original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.

The Budgeted Multi-armed Bandit Problem

The following coins problem is a version of a multi-armed bandit problem where one has to select from among a set of objects, say classifiers, after an experimentation phase that is constrained by a time or cost budget. The question is how to spend the budget. The problem involves pure exploration only, differentiating it from typical multi-armed bandit problems involving an exploration/exploitation tradeoff [BF85]. It is an abstraction of the following scenarios: choosing from among a set of alternative treatments after a fixed number of clinical trials, determining the best parameter settings for a program given a deadline that only allows a fixed number of runs; or choosing a life partner in the bachelor/bachelorette TV show where time is limited. We are interested in the computational complexity of the coins problem and/or efficient algorithms with approximation guarantees.

Contextual recommender problems [extended abstract]

The contextual recommender task is the problem of making useful offers, e.g., placing ads or related links on a web page, based on the context information, e.g., contents of the page and information about the user visiting, and information on the available alternatives, i.e., the advertisements or relevant links. In the case of ads for example, the goal is to select ads that result in high click rates, where the (ad) click rate is some unknown function of the attributes of the context and ad. We describe the task and make connections to related problems including recommender and multi-armed bandit problems.