Maksims Volkovs

BoltzRank: learning to maximize expected ranking gain

Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to optimize, as ranking performance is typically judged by metrics that are not smooth. In this paper we propose a new listwise approach to learning to rank. Our method creates a conditional probability distribution over rankings assigned to documents for a given query, which permits gradient ascent optimization of the expected value of some performance measure. The rank probabilities take the form of a Boltzmann distribution, based on an energy function that depends on a scoring function composed of individual and pairwise potentials. Including pairwise potentials is a novel contribution, allowing the model to encode regularities in the relative scores of documents; existing models assign scores at test time based only on individual documents, with no pairwise constraints between documents. Experimental results on the LETOR3.0 data set show that our method out-performs existing learning approaches to ranking.

Continuous data cleaning

In declarative data cleaning, data semantics are encoded as constraints and errors arise when the data violates the constraints. Various forms of statistical and logical inference can be used to reason about and repair inconsistencies (errors) in data. Recently, unified approaches that repair both errors in data and errors in semantics (the constraints) have been proposed. However, both data-only approaches and unified approaches are by and large static in that they apply cleaning to a single snapshot of the data and constraints. We introduce a continuous data cleaning framework that can be applied to dynamic data and constraint environments. Our approach permits both the data and its semantics to evolve and suggests repairs based on the accumulated evidence to date. Importantly, our approach uses not only the data and constraints as evidence, but also considers the past repairs chosen and applied by a user (user repair preferences). We introduce a repair classifier that predicts the type of repair needed to resolve an inconsistency, and that learns from past user repair preferences to recommend more accurate repairs in the future. Our evaluation shows that our techniques achieve high prediction accuracy and generate high quality repairs. Of independent interest, our work makes use of a set of data statistics that are shown to be sensitive to predicting particular repair types.

A flexible generative model for preference aggregation

Many areas of study, such as information retrieval, collaborative filtering, and social choice face the preference aggregation problem, in which multiple preferences over objects must be combined into a consensus ranking. Preferences over items can be expressed in a variety of forms, which makes the aggregation problem difficult. In this work we formulate a flexible probabilistic model over pairwise comparisons that can accommodate all these forms. Inference in the model is very fast, making it applicable to problems with hundreds of thousands of preferences. Experiments on benchmark datasets demonstrate superior performance to existing methods

Effective Latent Models for Binary Feedback in Recommender Systems

In many collaborative filtering (CF) applications, latent approaches are the preferred model choice due to their ability to generate real-time recommendations efficiently. However, the majority of existing latent models are not designed for implicit binary feedback (views, clicks, plays etc.) and perform poorly on data of this type. Developing accurate models from implicit feedback is becoming increasingly important in CF since implicit feedback can often be collected at lower cost and in much larger quantities than explicit preferences. The need for accurate latent models for implicit data was further emphasized by the recently conducted Million Song Dataset Challenge organized by Kaggle. In this challenge, the results for the best latent model were orders of magnitude worse than neighbor-based approaches, and all the top performing teams exclusively used neighbor-based models. We address this problem and propose a new latent approach for binary feedback in CF. In our model, neighborhood similarity information is used to guide latent factorization and derive accurate latent representations. We show that even with simple factorization methods like SVD, our approach outperforms existing models and produces state-of-the-art results.

Learning to rank by aggregating expert preferences

We present a general treatment of the problem of aggregating preferences from several experts into a consensus ranking, in the context where information about a target ranking is available. Specifically, we describe how such problems can be converted into a standard learning-to-rank one on which existing learning solutions can be invoked. This transformation allows us to optimize the aggregating function for any target IR metric, such as Normalized Discounted Cumulative Gain, or Expected Reciprocal Rank. When applied to crowdsourcing and meta-search benchmarks, our new algorithm improves on state-of-the-art preference aggregation methods.

CRF framework for supervised preference aggregation

We develop a flexible Conditional Random Field framework for supervised preference aggregation, which combines preferences from multiple experts over items to form a distribution over rankings. The distribution is based on an energy comprised of unary and pairwise potentials allowing us to effectively capture correlations between both items and experts. We describe procedures for learning in this modelnand demonstrate that inference can be done much more efficiently thannin analogous models. Experiments on benchmark tasks demonstrate significant performance gains over existing rank aggregation methods.

ConEx: a system for monitoring queries

We present a system, ConEx, for monitoring query execution in a relational database management system. ConEx offers a unified view of query execution, providing continuous visual feedback on the progress of the query, and the status of operators in the query evaluation plan. It incorporates novel techniques to dynamically estimate important parameters affecting query progress efficiently. We describe the design and features of ConEx, and discuss its technology.

Two-stage Model for Automatic Playlist Continuation at Scale

Automatic playlist continuation is a prominent problem in music recommendation. Significant portion of music consumption is now done online through playlists and playlist-like online radio stations. Manually compiling playlists for consumers is a highly time consuming task that is difficult to do at scale given the diversity of tastes and the large amount of musical content available. Consequently, automated playlist continuation has received increasing attention recently [1, 7, 11]. The 2018 ACM RecSys Challenge [14] is dedicated to evaluating and advancing current state-of-the-art in automated playlist continuation using a large scale dataset released by Spotify. In this paper we present our approach to this challenge. We use a two-stage model where the first stage is optimized for fast retrieval, and the second stage re-ranks retrieved candidates maximizing the accuracy at the top of the recommended list. Our team vl6 achieved 1st place in both main and creative tracks out of over 100 teams.