Iftah Gamzu, Hila Gonen, Gilad Kutiel, Ran Levy, Eugene Agichtein. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Voice shopping using natural language introduces new challenges related to customer queries, like handling mispronounced, misexpressed, and misunderstood queries. Voice null queries, which result in no offers, have negative impact on customers shopping experience. Query rewriting (QR) attempts to automatically replace null queries with alternatives that lead to relevant results. We present a new approach for pre-retrieval QR of voice shopping null queries. Our proposed QR framework first generates alternative queries using a search index-based approach that targets different potential failures in voice queries. Then, a machine-learning component ranks these alternatives, and the original query is amended by the selected alternative. We provide an experimental evaluation of our approach based on data logs of a commercial voice assistant and an e-commerce website, demonstrating that it outperforms several baselines by more than $22%$. Our evaluation also highlights an interesting phenomenon, showing that web shopping null queries are considerably different, and apparently easier to fix, than voice queries. This further substantiates the use of specialized mechanisms for the voice domain. We believe that our proposed framework, mapping tail queries to head queries, is of independent interest since it can be extended and applied to other domains.

The constant growth of machine-generated mail, which today consists of more than 90% of non-spam mail traffic, is a major contributor toinformation overload in email, where users become overwhelmed with a flood of messages from commercial entities. A large part of this traffic is often junk mail that the user would prefer not to receive. Surprisingly, nearly 95% of this traffic is in fact solicited by the users themselves in the form of subscriptions to mailing services. These subscriptions are many times unintentional. Although unsubscription option from such services is enforced by commercial laws, it is hardly actually used by users. We perform a large scale study ofunsubscribable traffic, namely, messages that provide unsubscription option to users. We consider users behavior over such traffic in Yahoo Web mail service, and demonstrate a significant gap between users low interest in this traffic, and their lack of active behavior in decreasing its load. We conjecture that the cause of this gap is the lack of an efficient and easily accessible mechanism that would help users to unsubscribe. We validate our conjecture with an online large scale experiment, where we provide users with a novel mail feature for managing unsubscribable traffic, based on personalized recommendations. The experiment demonstrates the imminent need that exists for such a mechanism.

Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind that mail extraction supports a variety of mail mining applications such as ad re-targeting, mail search, and mail summarization. We introduce new structural clustering methods that leverage the HTML structure that is common to messages generated by a same mass-sender script. We discuss how such structural clustering can be conducted at different levels of granularity, using either strict or flexible matching constraints, depending on the use cases.

We study a natural generalization of the correlation clustering problem to graphs in which the pairwise relations between objects are categorical instead of binary. This problem was recently introduced by Bonchi et al. under the name of chromatic correlation clustering, and is motivated by many real-world applications in data-mining and social networks, including community detection, link classification, and entity de-duplication. Our main contribution is a fast and easy-to-implement constant approximation framework for the problem, which builds on a novel reduction of the problem to that of correlation clustering. This result significantly progresses the current state of knowledge for the problem, improving on a previous result that only guaranteed linear approximation in the input size. We complement the above result by developing a linear programming-based algorithm that achieves an improved approximation ratio of 4. Although this algorithm cannot be considered to be practical, it further extends our theoretical understanding of chromatic correlation clustering. We also present a fast heuristic algorithm that is motivated by real-life scenarios in which there is a ground-truth clustering that is obscured by noisy observations. We test our algorithms on both synthetic and real datasets, like social networks data. Our experiments reinforce the theoretical findings by demonstrating that our algorithms generally outperform previous approaches, both in terms of solution cost and reconstruction of an underlying ground-truth clustering.

An instance of the generalized reordering buffer management problem consists of a service station that has k servers, each configured with a color, and a buffer of size b. The station needs to serve an online stream of colored items. Whenever an item arrives, it is stored in the buffer. At any point in time, a currently pending item can be served by switching a server to its color. The objective is to serve all items in a way that minimizes the number of servers color switches. This problem generalizes two well-studied online problems: the paging problem, which is the special case when b=1, and the reordering buffer problem, which is the special case when k=1. In this paper, we develop a randomized online algorithm that obtains a competitive ratio of O(sqrt(b).ln(k)). Note that this result beats the easy deterministic lower bound of k whenever b < k^(2-e). We complement our randomized approach by presenting a deterministic algorithm that attains a competitive ratio of O(min{k^2.ln(b),k.b}). We further demonstrate that if our deterministic algorithm can employ k/(1-d) servers where d is in (0,1), then it achieves a competitive ratio of O(min{ln(b/d^2),b/d}) against an optimal offline adversary that employs k servers.

We study the following balls and bins stochastic process: There is a buffer with B bins, and there is a stream of balls X = {X1, X2, ... ,XT} such that Xi is the number of balls that arrive before time i but after time i-1. Once a ball arrives, it is stored in one of the unoccupied bins. If all the bins are occupied then the ball is thrown away. In each time step, we select a bin uniformly at random, clear it, and gain its content. Once the stream of balls ends, all the remaining balls in the buffer are cleared and added to our gain. We are interested in analyzing the expected gain of this randomized process with respect to that of an optimal gain-maximizing strategy, which gets the same online stream of balls, and clears a ball from a bin, if exists, at any step. We name this gain ratio the loss of serving in the dark.

Signaling is an important topic in the study of asymmetric information in economic settings. In particular, the transparency of information available to a seller in an auction setting is a question of major interest. We introduce the study of signaling when conducting a second price auction of a probabilistic good whose actual instantiation is known to the auctioneer but not to the bidders. This framework can be used to model impressions selling in display advertising. We establish several results within this framework. First, we study the problem of computing a signaling scheme that maximizes the auctioneer's revenue in a Bayesian setting. We show that this problem is polynomially solvable for some interesting special cases, but computationally hard in general. Second, we establish a tight bound on the minimum number of signals required to implement an optimal signaling scheme. Finally, we show that at least half of the maximum social welfare can be preserved within such a scheme.

Brands and agencies use marketing as a tool to influence customers. One of the major decisions in a marketing plan deals with the allocation of a given budget among media channels in order to maximize the impact on a set of potential customers. A similar situation occurs in a social network, where a marketing budget needs to be distributed among a set of potential influencers in a way that provides high-impact.

We study the problem of ranking with submodular valuations. An instance of this problem consists of a ground set [m], and a collection of n monotone sub-modular set functions f1, …, fn, where each function fi : 2[m] → ℝ+. An additional input ingredient is a weight vector w ∊ ℝ+n. The goal is to find a linear ordering of the ground set elements that minimizes the weighted cover time of the functions. The cover time of a function is the minimal number of elements in the prefix of the linear ordering that form a set whose corresponding function value is greater than a unit threshold value. Our main result is an O(ln(1/ε))-approximation algorithm for the problem, where ε is the smallest nonzero marginal value that any function may gain from some element. Our algorithm orders the elements using an adaptive residual updates scheme, which may be of independent interest. We also prove that the problem is Ω(ln(1/ε))-hard to approximate, unless P = NP. This implies that the outcome of our algorithm is optimal up to constant factors.

The unsplittable flow problem is one of the most extensively studied optimization problems in the field of networking. An instance of it consists of an edge capacitated graph and a set of connection requests, each of which is associated with source and target vertices, a demand, and a value. The objective is to route a maximum value subset of requests subject to the edge capacities. It is a well known fact that as the capacities of the edges are larger with respect to the maximal demand among the requests, the problem can be approximated better. In particular, it is known that for sufficiently large capacities, the integrality gap of the corresponding integer linear program becomes 1 + ϵ, which can be matched by an algorithm that utilizes the randomized rounding technique. In this article, we focus our attention on the large capacities unsplittable flow problem in a game theoretic setting. In this setting, there are selfish agents, which control some of the requests characteristics, and may be dishonest about them. It is worth noting that in game theoretic settings many standard techniques, such as randomized rounding, violate certain monotonicity properties, which are imperative for truthfulness, and therefore cannot be employed. In light of this state of affairs, we design a monotone deterministic algorithm, which is based on a primal-dual machinery, which attains an approximation ratio of e / e -1, up to a disparity of ϵ away. This implies an improvement on the current best truthful mechanism, as well as an improvement on the current best combinatorial algorithm for the problem under consideration. Surprisingly, we demonstrate that any algorithm in the family of reasonable iterative path minimizing algorithms, cannot yield a better approximation ratio. Consequently, it follows that in order to achieve a monotone PTAS, if that exists, one would have to exert different techniques. We also consider the large capacities single-minded multi-unit combinatorial auction problem . This problem is closely related to the unsplittable flow problem since one can formulate it as a special case of the integer linear program of the unsplittable flow problem. Accordingly, we obtain a comparable performance guarantee by refining the algorithm suggested for the unsplittable flow problem.

We consider buffer management of unit packets with deadlines for a multi-port device with reconfiguration overhead. The goal is to maximize the throughput of the device, i.e., the number of packets delivered by their deadline. For a single port or with free reconfiguration, the problem reduces to the well-known packets scheduling problem, where the celebrated earliest-deadline-first (EDF) strategy is optimal 1-competitive. However, EDF is not 1-competitive when there is a reconfiguration overhead. We design an online algorithm that achieves a competitive ratio of 1 - o(1) when the ratio between the minimum laxity of the packets and the number of ports tends to infinity. This is one of the rare cases where one can design an almost 1-competitive algorithm. One ingredient of our analysis, which may be interesting on its own right, is a perturbation theorem on EDF for the classical packets scheduling problem. Specifically, we show that a small perturbation in the release and deadline times cannot significantly degrade the optimal throughput. This implies that EDF is robust in the sense that its throughput is close to the optimum even when the deadlines are not precisely known.

One of the most fundamental problems in web search is how to re-rank result web pages based on user logs. Most traditional models for re-ranking assume each query has a single intent. That is, they assume all users formulating the same query have similar preferences over the result web pages. It is clear that this is not true for a large portion of queries as different users may have different preferences over the result web pages. Accordingly, a more accurate model should assume that queries have multiple intents. In this paper, we introduce the multiple intents re-ranking problem. This problem captures scenarios in which some user makes a query, and there is no information about its real search intent. In such cases, one would like to re-rank the search results in a way that minimizes the efforts of all users in finding their relevant web pages. More formally, the setting of this problem consists of various types of users, each of which interested in some subset of the search results. Moreover, each user type has a non-negative profile vector. Consider some ordering of the search results. This order sets a position for each search result, and induces a position vector of the results relevant to each user type. The overhead of a user type is the dot product of its profile vector and its induced position vector. The goal is to order the search results as to minimize the average overhead of the users.

An instance of the sorting buffer problem consists of a metric space and a server, equipped with a finite-capacity buffer capable of holding a limited number of requests. An additional ingredient of the input is an online sequence of requests, each of which is characterized by a destination in the given metric space; whenever a request arrives, it must be stored in the sorting buffer. At any point in time, a currently pending request can be served by drawing it out of the buffer and moving the server to its corresponding destination. The objective is to serve all input requests in a way that minimizes the total distance traveled by the server. In this article, we focus our attention on instances of the problem in which the underlying metric is either an evenly-spaced line metric or a continuous line metric . Our main findings can be briefly summarized as follows. (1) We present a deterministic O (log n )-competitive algorithm for n -point evenly-spaced line metrics. This result improves on a randomized O (log 2 n )-competitive algorithm due to Khandekar and Pandit [2006b]. It also refutes their conjecture, stating that a deterministic strategy is unlikely to obtain a nontrivial competitive ratio. (2) We devise a deterministic O (log N log log N )-competitive algorithm for continuous line metrics, where N denotes the length of the input sequence. In this context, we introduce a novel discretization technique of independent interest. (3) We establish the first nontrivial lower bound for the evenly-spaced case, by proving that the competitive ratio of any deterministic algorithm is at least 2 + √3/√3 ≈ 2.154. This result settles, to some extent, an open question due to Khandekar and Pandit [2006b], who posed the task of attaining lower bounds on the achievable competitive ratio as a foundational objective for future research.

The unsplittable flow problem is one of the most extensively studied optimization problems in the field of networking. An instance of it consists of an edge capacitated graph and a set of connection requests, each of which is associated with source and target vertices, a demand, and a value. The objective is to route a maximum value subset of requests subject to the edge capacities. It is a well known fact that as the capacities of the edges are larger with respect to the maximal demand among the requests, the problem can be approximated better. In particular, it is known that for sufficiently large capacities, the integrality gap of the corresponding integer linear program becomes 1+ε, which can be matched by an algorithm that utilizes the randomized rounding technique.