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Proceedings ArticleDOI

MobiHerd: Towards Enabling Cost-Effective and Scalable Mobile Group Buying

TL;DR: This work proposes an end-to-end mobile group-buying system that can be used for targeted decentralized advertisement and discovery of group buying deals, and group formation to avail a deal.
Abstract: Group buying offers products at significantly reduced prices on the condition that a pre-specified number of buyers would make the purchase. Given the ever-increasing popularity of mobile devices and applications coupled with the typically high price-sensitivity of a significant percentage of users, group buying in mobile environments has the potential to attain dramatically increasing popularity. However, existing solutions for group buying typically involve web-based portals, which are not capable of handling user mobility. Hence, this work proposes an end-to-end mobile group-buying system that can be used for targeted decentralized advertisement and discovery of group buying deals, and group formation to avail a deal. The key contributions are three-fold. First, it proposes an ILP (Integer Linear Programming)-based optimal algorithm for the problem of efficiently forming groups of buyers with the objective of maximizing the overall utility of the solution. Second, it proposes a greedy algorithm for the same problem since solving ILP can take significant time for some problem instances. The greedy algorithm takes an input parameter, which can be tweaked to trade-off its optimality with its running time. Third, performance study shows that the proposed algorithms exhibit good performance in terms of the number of groups formed w.r.t. The requests in the system. Notably, the greedy algorithm provides near-optimal solution and runs significantly faster than the ILP-based optimal algorithm.
Citations
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Proceedings ArticleDOI
01 Nov 2018
TL;DR: It is proved that GAWA, a Group-buying based Auction mechanism for Wi-Fi Advertising among a venue owner, group leaders and advertisers, is computationally efficient, and possesses excellent economic properties such as individual rationality, budget balance, and truthfulness.
Abstract: The recent proliferation of public hotspots has given rise to Wi-Fi advertising where venue owners promote their business by pushing advertisers' advertisements on their hotspots. However, a small business usually has insufficient budget to make a purchase for a whole webpage. Therefore, in this paper, we propose GAWA, a Group-buying based Auction mechanism for Wi-Fi Advertising among a venue owner, group leaders and advertisers, which is composed of three phases. More specifically, in the first phase, we propose an algorithm to decide a group bid for each group leader and winning advertisers for each group. In the second phase, the venue owner assigns venues to group leaders by a novel winning group leader determination algorithm. In the third phase, the mechanism determines how much each winning group leader should charge each advertiser in the winning group. We prove that GAWA is computationally efficient, and possesses excellent economic properties such as individual rationality, budget balance, and truthfulness. We evaluate the proposed algorithms using large-scale simulations, and demonstrate the effectiveness and efficiency of our design when comparing with the state-of-the-art approaches.

2 citations


Cites background from "MobiHerd: Towards Enabling Cost-Eff..."

  • ...Another line of past literature [17]–[21], related to this paper, investigates group-buying mechanisms....

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References
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Proceedings ArticleDOI
08 Apr 2013
TL;DR: The dynamic ridesharing problem is formally defined, a large-scale taxi ridesh sharing service is proposed that efficiently serves real-time requests sent by taxi users and generates rideshared schedules that reduce the total travel distance significantly.
Abstract: Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).

487 citations


"MobiHerd: Towards Enabling Cost-Eff..." refers background in this paper

  • ...It can be used albeit possibly with modifications to form groups in carpooling [6], ride-sharing [7], impromptu flash mob [8] events for yoga, etc....

    [...]

Journal ArticleDOI
TL;DR: This work derives the monopolist's optimal group-buying schedule under varying conditions of heterogeneity in the demand regimes, and compares its profits with those that obtain under the more conventional posted-price mechanism.
Abstract: Web-based group-buying mechanisms are being widely used for both business-to-business (B2B) and business-to-consumer (B2C) transactions. We survey currently operational online group-buying markets, and then study this phenomenon using analytical models. We build on the literatures in information economics and operations management in our analytical model of a monopolist offering Web-based group-buying under different kinds of demand uncertainty. We derive the monopolist's optimal group-buying schedule under varying conditions of heterogeneity in the demand regimes, and compare its profits with those that obtain under the more conventional posted-price mechanism. We further study the impact ofproduction postponement by endogenizing the timing of the pricing and production decisions in a two-stage game between the monopolist and buyers. Our results have implications for firms' choice of price-discovery mechanisms in e-markets, and for the scheduling of production and pricing decisions in the presence (and absence) of scale economies of production.

388 citations


"MobiHerd: Towards Enabling Cost-Eff..." refers background in this paper

  • ...Group buying [1], [2], also known as collective buying, offers products and services at significantly reduced prices on the condition that a pre-specified number of products would be purchased....

    [...]

Proceedings ArticleDOI
05 Oct 2014
TL;DR: It is demonstrated that Foundry and flash teams enable crowdsourcing of a broad class of goals including design prototyping, course development, and film animation, in half the work time of traditional self-managed teams.
Abstract: We introduce flash teams, a framework for dynamically assembling and managing paid experts from the crowd. Flash teams advance a vision of expert crowd work that accomplishes complex, interdependent goals such as engineering and design. These teams consist of sequences of linked modular tasks and handoffs that can be computationally managed. Interactive systems reason about and manipulate these teams' structures: for example, flash teams can be recombined to form larger organizations and authored automatically in response to a user's request. Flash teams can also hire more people elastically in reaction to task needs, and pipeline intermediate output to accelerate completion times. To enable flash teams, we present Foundry, an end-user authoring platform and runtime manager. Foundry allows users to author modular tasks, then manages teams through handoffs of intermediate work. We demonstrate that Foundry and flash teams enable crowdsourcing of a broad class of goals including design prototyping, course development, and film animation, in half the work time of traditional self-managed teams.

214 citations

Proceedings ArticleDOI
28 May 2001
TL;DR: Simulation results show that, under most conditions, the proposed GroupBuyAuction scheme increases buyers' utility, and allows more buyers to obtain items compared to traditional group buying schemes, such as those used at existing commercial WWW sites.
Abstract: Buyer coalitions are beneficial in e-marketplaces because they allow buyers to take advantage of volume discounts. However, existing buyer coalition formation schemes do not provide buyers with any means to declare and match their preferences or to calculate the division of the surplus in a stable manner. Concepts and algorithms for coalition formation have been investigated in game theory and multi-agent systems research, but because of the computational complexity, they cannot deal with thousands of buyers which could join a coalition in practice. In this paper, we propose a new buyer coalition formation scheme GroupBuyAuction. At GroupBuyAuction, buyers form a group based on a category of items. A buyer can post an OR-asking for multiple items within a category. An OR-asking is a list of items indicating that the buyer would buy any one of the items in the list with some particular reservation price. Sellers bid volume discount prices. The group leader agent splits the group into sub groups (coalitions), selects a winning seller for each coalition, and calculates surplus division among buyers. We prove that this scheme guarantees the stability in surplus division within each coalition in terms of the core in game theory. Simulation results show that, under most conditions, our scheme increases buyers' utility, and allows more buyers to obtain items compared to traditional group buying schemes, such as those used at existing commercial WWW sites.

187 citations

Journal ArticleDOI
01 Apr 2010
TL;DR: A heuristic algorithm is proposed based on augmented greedy selections, along with a cost sharing rule satisfying certain stability properties for Combinatorial Coalition Formation (CCF), which allows buyers to announce reserve prices for combinations of items.
Abstract: A group-buying market may offer multiple items with non-additive values (i.e., items may be complementary or substitutable), to buyers who are often heterogeneous in their item valuations. In such a situation, the formation of buying groups should concentrate buyers for common items while taking into consideration buyers' heterogeneous preferences over item bundles. Also, it should permit non-uniform cost sharing among buyers in the same group, which benefits all buyers by drawing more group-buying participants. We introduce the concept of Combinatorial Coalition Formation (CCF), which allows buyers to announce reserve prices for combinations of items. These reserve prices, along with the sellers' price-quantity curves for each item, are used to determine the formation of buying groups for each item. Moreover, buyers in the same group may not necessarily all pay the same price. The objective of CCF is to maximize buyers' total surplus. Determining the optimal coalition configuration in CCF is NP-hard, and the stability of such a configuration relies on the cost sharing rule within each group. We thus propose a heuristic algorithm for CCF based on augmented greedy selections, along with a cost sharing rule satisfying certain stability properties. Simulation results show that our approximate algorithm generates fairly good solutions compared to the optimal results, and is greatly superior to a simpler distributed approach. Furthermore, our algorithm's performance is enhanced when items are complementary or strongly substitutable, especially in settings when the prices decrease either rapidly or slowly with the quantities. Evaluations of the sellers' revenue under CCF demonstrate that sellers should offer a more gradually decreasing price-quantity curve for complementary or strongly substitutable items, and a more abruptly decreasing curve for weakly substitutable items. In addition, sellers may benefit from greater sales generated by simpler price-quantity curves with fewer steps.

92 citations