A Click Sequence Model for Web Search

Getting a better understanding of user behavior is important for advancing information retrieval systems. Existing work focuses on modeling and predicting single interaction events, such as clicks. In this paper, we for the first time focus on modeling and predicting sequences of interaction events. And in particular, sequences of clicks. We formulate the problem of click sequence prediction and propose a click sequence model (CSM) that aims to predict the order in which a user will interact with search engine results. CSM is based on a neural network that follows the encoder-decoder architecture. The encoder computes contextual embeddings of the results. The decoder predicts the sequence of positions of the clicked results. It uses an attention mechanism to extract necessary information about the results at each timestep. We optimize the parameters of CSM by maximizing the likelihood of observed click sequences. We test the effectiveness of CSM on three new tasks: (i) predicting click sequences, (ii) predicting the number of clicks, and (iii) predicting whether or not a user will interact with the results in the order these results are presented on a search engine result page (SERP). Also, we show that CSM achieves state-of-the-art results on a standard click prediction task, where the goal is to predict an unordered set of results a user will click on.

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title: Implicit Feedback for Recommender Systems
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title: Beyond Ranking: Optimizing Whole-Page Presentation
abstract: Modern search engines aggregate results from different verticals: webpages, news, images, video, shopping, knowledge cards, local maps, etc. Unlike "ten blue links", these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional "ranked list" formulation in ad hoc search. Therefore, finding proper presentation for a gallery of heterogeneous results is critical for modern search engines. We propose a novel framework that learns the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content-aware, i.e. tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can learn its own result presentation strategy purely from data, without even knowing the "probability ranking principle".

title: Estimating interleaved comparison outcomes from historical click data
abstract: Interleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval evaluation methods that require expensive explicit judgments. A major limitation of these methods is that they assume access to live data, meaning that new data must be collected for every pair of rankers compared. We investigate the use of previously collected click data (i.e., historical data) for interleaved comparisons. We start by analyzing to what degree existing interleaved comparison methods can be applied and find that a recent probabilistic method allows such data reuse, even though it is biased when applied to historical data. We then propose an interleaved comparison method that is based on the probabilistic approach but uses importance sampling to compensate for bias. We experimentally confirm that probabilistic methods make the use of historical data for interleaved comparisons possible and effective.

title: An experimental comparison of click position-bias models
abstract: Search engine click logs provide an invaluable source of relevance information, but this information is biased. A key source of bias is presentation order: the probability of click is influenced by a document's position in the results page. This paper focuses on explaining that bias, modelling how probability of click depends on position. We propose four simple hypotheses about how position bias might arise. We carry out a large data-gathering effort, where we perturb the ranking of a major search engine, to see how clicks are affected. We then explore which of the four hypotheses best explains the real-world position effects, and compare these to a simple logistic regression model. The data are not well explained by simple position models, where some users click indiscriminately on rank 1 or there is a simple decay of attention over ranks. A â cascade' model, where users view results from top to bottom and leave as soon as they see a worthwhile document, is our best explanation for position bias in early ranks

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abstract: Finding audiovisual material for reuse in new programs is an important activity for news producers, documentary makers, and other media professionals. Such professionals are typically served by an audiovisual broadcast archive. We report on a study of the transaction logs of one such archive. The analysis includes an investigation of commercial orders made by the media professionals and a characterization of sessions, queries, and the content of terms recorded in the logs. One of our key findings is that there is a strong demand for short pieces of audiovisual material in the archive. In addition, while searchers are generally able to quickly navigate to a usable audiovisual broadcast, it takes them longer to place an order when purchasing a subsection of a broadcast than when purchasing an entire broadcast. Another key finding is that queries predominantly consist of (parts of) broadcast titles and of proper names. Our observations imply that it may be beneficial to increase support for fine-grained access to audiovisual material, for example, through manual segmentation or content-based analysis.

title: Evaluating implicit measures to improve web search
abstract: Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis (“gene analysis”). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions.

title: Modeling dwell time to predict click-level satisfaction
abstract: Clicks on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown that clicks are affected by position bias, caption bias, and other factors. A popular heuristic for reducing this noise is to only consider clicks with long dwell time, usually equaling or exceeding 30 seconds. The rationale is that the more time a searcher spends on a page, the more likely they are to be satisfied with its contents. However, having a single threshold value assumes that users need a fixed amount of time to be satisfied with any result click, irrespective of the page chosen. In reality, clicked pages can differ significantly. Pages have different topics, readability levels, content lengths, etc. All of these factors may affect the amount of time spent by the user on the page. In this paper, we study the effect of different page characteristics on the time needed to achieve search satisfaction. We show that the topic of the page, its length and its readability level are critical in determining the amount of dwell time needed to predict whether any click is associated with satisfaction. We propose a method to model and provide a better understanding of click dwell time. We estimate click dwell time distributions for SAT (satisfied) or DSAT (dissatisfied) clicks for different click segments and use them to derive features to train a click-level satisfaction model. We compare the proposed model to baseline methods that use dwell time and other search performance predictors as features, and demonstrate that the proposed model achieves significant improvements.

title: Large-scale validation and analysis of interleaved search evaluation
abstract: Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture of its efficiency and effectiveness is still lacking. In this paper we extend and combine the body of empirical evidence regarding interleaving, and provide a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature. In particular, we analyze the agreement of interleaving with manual relevance judgments and observational implicit feedback measures, estimate the statistical efficiency of interleaving, and explore the relative performance of different interleaving variants. We also show how to learn improved credit-assignment functions for clicks that further increase the sensitivity of interleaving.

title: Predicting Search Satisfaction Metrics with Interleaved Comparisons
abstract: The gold standard for online retrieval evaluation is AB testing. Rooted in the idea of a controlled experiment, AB tests compare the performance of an experimental system (treatment) on one sample of the user population, to that of a baseline system (control) on another sample. Given an online evaluation metric that accurately reflects user satisfaction, these tests enjoy high validity. However, due to the high variance across users, these comparisons often have low sensitivity, requiring millions of queries to detect statistically significant differences between systems. Interleaving is an alternative online evaluation approach, where each user is presented with a combination of results from both the control and treatment systems. Compared to AB tests, interleaving has been shown to be substantially more sensitive. However, interleaving methods have so far focused on user clicks only, and lack support for more sophisticated user satisfaction metrics as used in AB testing. In this paper we present the first method for integrating user satisfaction metrics with interleaving. We show how interleaving can be extended to (1) directly match user signals and parameters of AB metrics, and (2) how parameterized interleaving credit functions can be automatically calibrated to predict AB outcomes. We also develop a new method for estimating the relative sensitivity of interleaving and AB metrics, and show that our interleaving credit functions improve agreement with AB metrics without sacrificing sensitivity. Our results, using 38 large-scale online experiments en- compassing over 3 billion clicks in a web search setting, demonstrate up to a 22% improvement in agreement with AB metrics (constituting over a 50% error reduction), while maintaining sensitivity of one to two orders of magnitude above the AB tests. This paves the way towards more sensitive and accurate online evaluation.

title: Evaluating Personal Assistants on Mobile devices
abstract: The iPhone was introduced only a decade ago in 2007 but has fundamentally changed the way we interact with online information. Mobile devices differ radically from classic command-based and point-and-click user interfaces, now allowing for gesture-based interaction using fine-grained touch and swipe signals. Due to the rapid growth in the use of voice-controlled intelligent personal assistants on mobile devices, such as Microsoft's Cortana, Google Now, and Apple's Siri, mobile devices have become personal, allowing us to be online all the time, and assist us in any task, both in work and in our daily lives, making context a crucial factor to consider. 
Mobile usage is now exceeding desktop usage, and is still growing at a rapid rate, yet our main ways of training and evaluating personal assistants are still based on (and framed in) classical desktop interactions, focusing on explicit queries, clicks, and dwell time spent. However, modern user interaction with mobile devices is radically different due to touch screens with a gesture- and voice-based control and the varying context of use, e.g., in a car, by bike, often invalidating the assumptions underlying today's user satisfaction evaluation. 
There is an urgent need to understand voice- and gesture-based interaction, taking all interaction signals and context into account in appropriate ways. We propose a research agenda for developing methods to evaluate and improve context-aware user satisfaction with mobile interactions using gesture-based signals at scale.

title: Incorporating revisiting behaviors into click models
abstract: Click-through behaviors are treated as invaluable sources of user feedback and they have been leveraged in several commercial search engines in recent years. However, estimating unbiased relevance is always a challenging task because of position bias. To solve this problem, many researchers have proposed a variety of assumptions to model click-through behaviors. Most of these models share a common examination hypothesis, which is that users examine search results from the top to the bottom. Nevertheless, this model cannot draw a complete picture of information-seeking behaviors. Many eye-tracking studies find that user interactions are not sequential but contain revisiting patterns. If a user clicks on a higher ranked document after having clicked on a lower-ranked one, we call this scenario a revisiting pattern, and we believe that the revisiting patterns are important signals regarding a user's click preferences. This paper incorporates revisiting behaviors into click models and introduces a novel click model named Temporal Hidden Click Model (THCM). This model dynamically models users' click behaviors with a temporal order. In our experiment, we collect over 115 million query sessions from a widely-used commercial search engine and then conduct a comparative analysis between our model and several state-of-the-art click models. The experimental results show that the THCM model achieves a significant improvement in the Normalized Discounted Cumulative Gain (NDCG), the click perplexity and click distributions metrics.

title: Does That Mean You're Happy?: RNN-based Modeling of User Interaction Sequences to Detect Good Abandonment
abstract: Queries for which there are no clicks are known as abandoned queries. Differentiating between good and bad abandonment queries has become an important task in search engine evaluation since it allows for better measurement of search engine features that do not require users to click. Examples of these features include answers on the SERP and detailed Web result snippets. In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment. To do this, we study the behavior patterns on a labeled dataset of abandoned queries and find that they differ in several ways, such as in the number of user interactions and the nature of those interactions. Based on this insight, we frame good abandonment detection as a sequence classification problem. We use a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to model the sequence of user interactions and show that it performs significantly better than other baselines when detecting good abandonment, achieving 71% accuracy. Our findings have implications for search engine evaluation.

title: Robust models of mouse movement on dynamic web search results pages
abstract: Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs where cursor tracking was enabled, limiting their utility for informing the design of new SERPs. In this work, we develop robust, log-based mouse movement models capable of estimating searcher attention on novel SERP arrangements. These models can help improve SERP design by anticipating searchers' engagement patterns given a proposed arrangement. We demonstrate the efficacy of our method using a large set of mouse-tracking data collected from two independent commercial search engines.

title: Understanding web browsing behaviors through Weibull analysis of dwell time
abstract: Dwell time on Web pages has been extensively used for various information retrieval tasks. However, some basic yet important questions have not been sufficiently addressed, eg, what distribution is appropriate to model the distribution of dwell times on a Web page, and furthermore, what the distribution tells us about the underlying browsing behaviors. In this paper, we draw an analogy between abandoning a page during Web browsing and a system failure in reliability analysis, and propose to model the dwell time using the Weibull distribution. Using this distribution provides better goodness-of-fit to real world data, and it uncovers some interesting patterns of user browsing behaviors not previously reported. For example, our analysis reveals that Web browsing in general exhibits a significant "negative aging" phenomenon, which means that some initial screening has to be passed before a page is examined in detail, giving rise to the browsing behavior that we call "screen-and-glean." In addition, we demonstrate that dwell time distributions can be reasonably predicted purely based on low-level page features, which broadens the possible applications of this study to situations where log data may be unavailable.

title: Time-Aware Click Model
abstract: Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users’ examination behavior, most existing click models are position based, assuming that users examine results from top to bottom in a linear fashion. Although these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users’ perceived relevance and search satisfaction. Incorporating temporal information may be important to improve performance of user click models for Web searches. In this article, we investigate the problem of properly incorporating temporal information into click models. We first carry out a laboratory eye-tracking study to analyze users’ examination behavior in different click sequences and find that the user common examination path among adjacent clicks is linear. Next, we analyze the user dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold (e.g., 30 seconds) to distinguish relevant/irrelevant results. Finally, we propose a novel time-aware click model (TACM), which captures the temporal information of user behavior. We compare the TACM to several existing click models using two real-world search engine logs. Experimental results show that the TACM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).

title: Inferring search behaviors using partially observable Markov (POM) model
abstract: This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant of the hidden Markov model in which all the hidden state transitions do not necessarily emit observable events. This property of POM is used to model, as the hidden process, a common search behavior that users would read and skip search results, leaving no observable user actions to record in the search logs. The Markov nature of the model further lends support to cope with the facts that a single observed sequence can be probabilistically associated with many hidden sequences that have variable lengths, and the search results can be read in various temporal orders that are not necessarily reflected in the observed sequence of user actions. To tackle the implementation challenges accompanying the flexibility and analytic powers of POM, we introduce segmental Viterbi algorithm based on segmental decoding and Viterbi training to train the POM model parameters and apply them to uncover hidden processes from the search logs. To validate the model, the latent variables modeling the browsing patterns on the search result page are compared with the experimental data of the eye tracking stu-dies. The close agreements suggest that the search logs do contain rich information of user behaviors in browsing the search result page even though they are not directly observable, and that using POM to understand these sophisticated search behaviors is a promising approach.

title: Constructing an Interaction Behavior Model for Web Image Search
abstract: User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For example, image search results are self-contained, so that users do not need to click the results to view the landing page as in general Web search, which generates sparse click data. Also, two-dimensional result placement instead of a linear result list makes browsing behaviors more complex. Thus, it is hard to apply standard user behavior models (e.g., click models) developed for general Web search to Web image search. In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log. We then propose a novel interaction behavior model, called grid-based user browsing model (GUBM), whose design is motivated by observations from our data analysis. GUBM can both capture users' interaction behavior, including cursor hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1) It is based on an unsupervised learning method and does not need manually annotated data for training. (2) It is based on user interaction features on search engine result pages (SERPs) and is easily transferable to other scenarios that have a grid-based interface such as video search engines. We conduct extensive experiments to test the performance of our model using a large-scale commercial image search log. Experimental results show that in terms of behavior prediction (perplexity), and topical relevance and image quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms state-of-the-art baseline models as well as the original ranking. We make the implementation of GUBM and related datasets publicly available for future studies.

title: Temporal click model for sponsored search
abstract: Previous studies on search engine click modeling have identified two presentation factors that affect users' behavior: (1) position bias: the same result will get a different number of clicks when displayed in different positions and (2) externalities: the same result might get more clicks when displayed with results of relatively lower quality than when shown with higher quality results. In this paper we focus on analyzing the sequence of user actions to model users' click behavior on sponsored listings shown on the search results page. We first show that temporal click sequences are good indicators of externalities in the advertising domain. We then describe the positional rationality hypothesis to explain both the position bias and the externalities, and based on this hypothesis we further propose the temporal click model (TCM), a Bayesian framework that is scalable and computationally efficient. To the best of our knowledge, this is the first attempt in the literature to estimate positional bias, externalities and unbiased user-perceived ad quality from user click logs in a combined model. We finally evaluate the proposed model on two real datasets, each containing over 100 million ad impressions obtained from a commercial search engine. The experimental results show that TCM outperforms two other competitive methods at click prediction.

title: Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
abstract: Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.

title: Product Quantization for Nearest Neighbor Search
abstract: This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.

title: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration
abstract: For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.