AI-ASSISTED GESTURE NAVIGATION FOR COMPUTING DEVICES

A computing device (e.g., a smartphone, a laptop computer, a tablet computer, a smartwatch, etc.) may use a machine learning model to classify user inputs as a back gesture for navigating with respect to graphical user interfaces (GUI) of the computing device. The computing device may apply a machine learning model to input data associated with the user input (e.g., (x,y) coordinates of the user inputs) and a context of the computing device (e.g., an application (“app”) that is currently executing on the computing device, the width of the computing device, the orientation of the computing device, etc.) to determine a degree of likelihood of the user input being a back gesture. If the degree of likelihood of the user input being a back gesture satisfies a threshold, the computing device may execute a back action associated with the back gesture. If the degree of likelihood does not satisfy the threshold, the computing device may execute a different action or may discard the user input. The machine learning model may be trained on a computing system (e.g., a remote server) distinct from the computing device while the trained machine learning model may be stored at the computing device

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.Comment: CIKM 201

PROACTIVE CONTEXTUAL INFORMATION

A computing device (e.g., a cellular phone, a smartphone, a desktop computer, a laptop computer, a tablet computer, a portable gaming device, a watch, etc.) may select and display one or more information objects based on contextual information (e.g., user behavior information, device behavior information, interaction information, user information, preference information, time information, news information, etc.) associated with user interactions (e.g., applications initialized by the user, instructions provided by the user, information requested by the user, etc.) with the computing device over time. For example, the computing device may utilize this contextual information to select, based on the current context of the computing device, one or more information objects that may be relevant or of interest to the user and output such information objects to the user. The computing device may present such information objects to the user at appropriate times and/or locations based on the contextual information and on various screens (e.g., always-on screen, lock screen, home screen, etc.) of the computing device. In some cases, the computing device may share an indication of the selected information objects to other devices associated with the user so that the other devices may similarly display the information objects

Domain Adaptation for Enterprise Email Search

In the enterprise email search setting, the same search engine often powers multiple enterprises from various industries: technology, education, manufacturing, etc. However, using the same global ranking model across different enterprises may result in suboptimal search quality, due to the corpora differences and distinct information needs. On the other hand, training an individual ranking model for each enterprise may be infeasible, especially for smaller institutions with limited data. To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise. In particular, we propose a novel application of the Maximum Mean Discrepancy (MMD) approach to information retrieval, which attempts to bridge the gap between the global data distribution and the data distribution for a given individual enterprise. We conduct a comprehensive set of experiments on a large-scale email search engine, and demonstrate that the MMD approach consistently improves the search quality for multiple individual domains, both in comparison to the global ranking model, as well as several competitive domain adaptation baselines including adversarial learning methods.Comment: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva

Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.Comment: 16 pages, 5 figure

Systematic optimization of search engines for difficult queries

With the advent of Web, text information is being generated across the globe at an unfathomable rate and covering countless topics. This dramatic growth in text information and the increasing number of ways people can utilize it has influenced our daily lives in fundamental and profound ways. The widespread and multi-purposed use of search engines is one example of how transformed our lives have become in relation to text information. Although the vast majority of people's information needs can be served very successfully by current search engines, there is still a considerable number of queries that even the best search engines perform poorly on. In this dissertation, we propose a method of optimizing a search engine to better handle such “difficult queries”, which addresses issues and opportunities at three different stages of an interactive search. Specifically, we propose to improve search quality for difficult queries by: (1) Bridging the vocabulary gap by defining a semantic smoothing language model. A query can be difficult because it does not contain the optimal choice of related terms, or lacks sufficient discriminative terms. In the pre-retrieval stage, using semantic smoothing during query formulation can mitigate the effect of those omissions. (2) Incorporating user negative feedback, i.e., improving a search engine by learning from user feedback in the post-retrieval stage. When a query is so difficult that all the top retrieved results (e.g., top 10) are completely irrelevant, the feedback that a user can provide is solely negative. We propose a generalized optimization framework to learn from feedback on non-relevant documents to prune extensively, but carefully, a large number of non-relevant documents from the top of the ranking list. (3) Balancing priorities when learning from user interaction in order to optimize the whole session. When presenting the search results to the user, there is a tradeoff between promoting those with the highest immediate utility and promoting those with the best potential for collecting feedback information, which can be used to better serve the user over the course of the session (interactive search stage). We frame this tradeoff as a problem of optimizing the diversification of search results, and we propose a machine learning approach that adaptively optimizes each individual user query such that we maximize the overall utility of the entire session. In summary, this dissertation is expected to advance the state of the art of search engines by providing a suite of novel search algorithms, which use the listed approaches to improve a search engine's ability to handle difficult queries

Expert finding by means of plausible inferences

Expert finding has become an important retrieval task. Expert finding is about finding people rather than documents and the goal is to retrieve a ranked list of candidates/experts with expertise on a given topic. In this paper, we describe an expert- finding system that reasons about the relevance of a candidate to a given expertise area. The system utilizes plausible inferences to infer the relevance of a candidate to a given topic. Experiments are conducted using the TREC 2006 enterprise track text collection. The results indicate the usefulness of our approach

Estimation of Statistical Translation Models Based on Mutual Information for Ad Hoc Information Retrieval

As a principled approach to capturing semantic relations of words in information retrieval, statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. Existing work has relied on training on synthetic queries generated based on a document collection. However, this method is computationally expensive and does not have a good coverage of query words. In this paper, we propose an alternative way to estimate a translation model based on normalized mutual information between words, which is less computationally expensive and has better coverage of query words than the synthetic query method of estimation. We also propose to regularize estimated translation probabilities to ensure sufficient probability mass for self-translation. Experiment results show that the proposed mutual information-based estimation method is not only more efficient, but also more effective than the synthetic query-based method, and it can be combined with pseudo-relevance feedback to further improve retrieval accuracy. The results also show that the proposed regularization strategy is effective and can improve retrieval accuracy for both synthetic query-based estimation and mutual information-based estimation