Multiple Simultaneous Responses For Instant Messaging

A system and method are disclosed for multiple responses in instant messaging conversations. The first part of the system is a machine learning algorithm to predict and identify cases in which a second input box would be needed. The second aspect is a mechanism in the chat or instant messaging application to show an additional text input box. The system includes an algorithm that could collect training datasets from the user’s messaging log. From the collected data, a binary classification model could be trained to classify such cases. When the messaging system identifies a similar case it would open a vanish input box so that the user could write his response for the newly received message. Once the response is sent, the new vanish input box would disappear and the user is returned to the original message. The system could be leveraged in any instant messaging solution

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure

LambdaLoss: Metric-Driven Loss for Learning-to Rank

How to directly optimize ranking metrics such as Normalized Dis­counted Cumulative Gain (NDCG) is an interesting but challenging problem, because ranking metrics are either flat or discontinuous ev­erywhere. Among existing approaches, LambdaRank is a novel algo­rithm that incorporates metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For ex­ample, what is the underlying loss that LambdaRank optimizes for? Due to this, it is unclear whether LambdaRank will always converge. In this paper, we present a well-defined loss for LambdaRank in a probabilistic framework and show that LambdaRank is a special configuration in our framework. This framework, which we call LambdaLoss, provides theoretical justification for Lamb-daRank. Furthermore, we propose a few more metric-driven loss functions in our LambdaLoss framework. Our loss functions have clear connection to ranking metrics and can be optimized in our framework efficiently. Experiments on three publicly available data sets show that our methods significantly outperform the state-of-the-art learning-to-rank algorithms. This confirms both the theo­retical soundness and the practical effectiveness of the LambdaLoss framework

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank.Comment: KDD 201

Constructing Travel Itineraries from Tagged Geo-Temporal Breadcrumbs

Vacation planning is a frequent laborious task which requires skilled interaction with a multitude of resources. This paper develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. In particular, the popular rich media sharing site, Flickr, allows photos to be stamped by the date and time of when they were taken, and be mapped to Points Of Interest (POIs) by latitude-longitude information as well as semantic metadata (e.g., tags) that describe them. Our extensive user study on a “crowd-sourcing ” marketplace (Amazon Mechanical Turk), indicates that high quality itineraries can be automatically constructed from Flickr data, when compared against popular professionally generated bus tours

Automatic Construction of Travel Itineraries using Social Breadcrumbs

Vacation planning is one of the frequent—but nonetheless laborious—tasks that people engage themselves with online; requiring skilled interaction with a multitude of resources. This paper constructs intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. For example, the popular rich media sharing site, Flickr, allows photos to be stamped by the time of when they were taken and be mapped to Points Of Interests (POIs) by geographical (i.e. latitudelongitude) and semantic (e.g., tags) metadata. Leveraging this information, we construct itineraries following a two-step approach. Given a city, we first extract photo streams of individual users. Each photo stream provides estimates on where the user was, how long he stayed at each place, and what was the transit time between places. In the second step, we aggregate all user photo streams into a POI graph. Itineraries are then automatically constructed from the graph based on the popularity of the POIs and subject to the user’s time and destination constraints. We evaluate our approach by constructing itineraries for several major cities and comparing them, through a“crowdsourcing” marketplace (Amazon Mechanical Turk), against itineraries constructed from popular bus tours that are professionally generated. Our extensive survey-based user studies over about 450 workers on AMT indicate that high quality itineraries can be automatically constructed from Flickr data

Birational geometry of singular Fano hypersurfaces of index two

For a Zariski general (regular) hypersurface V of degree M in the