29,035 research outputs found
Item Retrieval as Utility Estimation
Retrieval systems have greatly improved over the last half century, estimating relevance to a latent user need in a wide variety of areas. One area that has not enjoyed such advancements is searching for items by attribute values, a common activity in e-commerce and science, particularly given numeric values. Existing item retrieval systems assume the user has a firm grasp of their own desires and can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database. A contrasting approach would be to estimate how well items match the user?s latent desires and return items ranked by this estimation. Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes. We evaluate our novel approach, the de-facto standard of Boolean retrieval, and several models proposed in the literature, in two user studies using Amazon Mechanical Turk. We use a competitive game to motivate test subjects and compare methods based on the results of the subjects? initial query and their success in the game. In our experiments, our new method signi cantly outperformed the others, whereas the Boolean approaches had the worst performance
Optimal Information Retrieval with Complex Utility Functions
Existing retrieval models all attempt to optimize one single utility function, which is often based on the topical relevance of a document with respect to a query. In real applications, retrieval involves more complex utility functions that may involve preferences on several different dimensions. In this paper, we present a general optimization framework for retrieval with complex utility functions. A query language is designed according to this framework to enable users to submit complex queries. We propose an efficient algorithm for retrieval with complex utility functions based on the a-priori algorithm. As a case study, we apply our algorithm to a complex utility retrieval problem in distributed IR. Experiment results show that our algorithm allows for flexible tradeoff between multiple retrieval criteria. Finally, we study the efficiency issue of our algorithm on simulated data
Unbiased Comparative Evaluation of Ranking Functions
Eliciting relevance judgments for ranking evaluation is labor-intensive and
costly, motivating careful selection of which documents to judge. Unlike
traditional approaches that make this selection deterministically,
probabilistic sampling has shown intriguing promise since it enables the design
of estimators that are provably unbiased even when reusing data with missing
judgments. In this paper, we first unify and extend these sampling approaches
by viewing the evaluation problem as a Monte Carlo estimation task that applies
to a large number of common IR metrics. Drawing on the theoretical clarity that
this view offers, we tackle three practical evaluation scenarios: comparing two
systems, comparing systems against a baseline, and ranking systems. For
each scenario, we derive an estimator and a variance-optimizing sampling
distribution while retaining the strengths of sampling-based evaluation,
including unbiasedness, reusability despite missing data, and ease of use in
practice. In addition to the theoretical contribution, we empirically evaluate
our methods against previously used sampling heuristics and find that they
generally cut the number of required relevance judgments at least in half.Comment: Under review; 10 page
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
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