The article reviews the book “Information Rules: A Strategic Guide to the Network Economy,” by Carl Shapiro and Hal R. Varian.

Information Rules: A Strategic Guide to the Network Economy

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

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Research-paper recommender systems: a literature survey

This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce.

Agent-Mediated Electronic Commerce

Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified

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Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Online auctions are becoming an increasingly important channel for electronic commerce. There exist more than 150 online auction sites on the Internet. It is difficult for users to attend, monitor, and bid at multiple auction sites simultaneously. The authors propose BiddingBot which is a multi agent system that supports users in attending, monitoring, and bidding in multiple auctions. BiddingBot monitors prices of goods in several online auction sites to get reasonable market prices of goods, and uses a new cooperative bidding mechanism to effectively bid in auctions.

BiddingBot: a multiagent support system for cooperative bidding in multiple auctions

We present a method for multi-agent negotiation for implementing a distributed meeting scheduler. In the meeting scheduler, an agent negotiates with other agents about making a public schedule by referring user's private schedules and preferences. We propose a new persuasion method for multi-agent negotiation for reflecting private preferences. We call the method multiple negotiations. In order to reach a consensus effectively, we propose an effective preference revision mechanism based on the multi attribute utility theory. We have implemented a distributed meeting scheduler to show how effectively the multiple negotiations can be used. The result shows that the multiple negotiations are effective in supporting group decision-making for scheduling a meeting.

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Multiple negotiations among agents for a distributed meeting scheduler

Electronic commerce is becoming an increasingly important channel for retail commerce. There are a large number of e-commerce sites on the Internet. Intelligent agents have been studied very widely in the field of artificial intelligence and multi-agent systems. There have been several researches on agent-mediated electronic commerce. In particular, we focus on volume discount mechanism based on agent-mediated electronic commerce. In this paper, we propose a new group buy scheme for agent-mediated electronic markets. In this scheme, we introduce the concept of sequential decisionmaking for seller agents and buyer agents. Further, we propose a cooperation mechanism among seller agents based on exchange items in stock. We implemented an agent-mediated electronic markets based on the group buy scheme. The result shows that the new group buy scheme is effective in supporting electronic commerce based on volume discount.

A Group Buy Protocol based on Coalition Formation for Agent-mediated E-Commerce

Persuasion among Agents: An Approach to Implementing a Group Decision System Based on Multi-Agent Negotiation.

Volume discount is seen as an effective form of electronic commerce and a promising field for applying agent technologies. In current volume discount mechanisms, items are not allocated efficiently to buyers. Namely, social surplus is not maximum in existing volume discount schemes. To solve this problem, we propose a volume discount mechanism based on the seller's reservation price and the payment adjustment value. First, a seller registers his/her items with the evaluation value functions. The seller's evaluation value is sealed and each buyer bids his/her evaluation value as sealed bid. After the deadline, the mechanism determines the allocation of bundles of items. A tentative price is decided and the payment adjustment value is calculated. Finally, the payment amount is calculated. Our mechanism has some key advantages. First, the mechanism is Pareto efficient. Second, our mechanism is a strategy-proof mechanism, that is, it has the incentive compatibility. Third, our mechanism provides individual rationality. Fourth, our mechanism is made based on the volume discount system, where the seller can give a signal indicating a discount for buyers. Finally, our mechanism weakens the influence of false name bids.

Toramatsu Shintani

Papers

BiddingBot: a multiagent support system for cooperative bidding in multiple auctions

Multiple negotiations among agents for a distributed meeting scheduler

A Group Buy Protocol based on Coalition Formation for Agent-mediated E-Commerce

Persuasion among Agents: An Approach to Implementing a Group Decision System Based on Multi-Agent Negotiation.

A volume discount-based allocation mechanism in group buying