Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

Collaborative filtering with facial expressions for online video recommendation

Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation.     

Privacy-preserving multi-criteria collaborative filtering

Privacy-preserving collaborative filtering schemes focus on eliminating the privacy threats inherent in single preference values, and the privacy risks in the multi-criteria preference domain are disregarded. In this work, we introduce randomized perturbation-based privacy-preserving approaches for multi-criteria collaborative filtering systems. Initially, the privacy protection methods efficiently used in traditional single-criterion systems are adapted onto multi-criteria ratings. However, these systems require intelligent protection mechanisms that are flexible and adapting to the structure of each sub-criterion. To achieve such a goal, we introduce a novel privacy-preserving protocol by adapting an entropy-based randomness determination procedure that can recover accuracy losses. The proposed protocol adjusts privacy-controlling parameters concerning the information inherent in each criterion. We experimentally evaluate the proposed schemes on three subsets of Yahoo!Movies multi-criteria preference dataset to demonstrate the effects of the proposed privacy-preserving schemes on both user privacy levels and prediction accuracy for differing sparsity rates. According to the obtained experimental outcomes, the proposed entropy-based privacy-preserving scheme can produce significantly more accurate predictions while maintaining an identical level of privacy provided by the traditional privacy protection scenario. The experimental results also confirm that the novel entropy-based privacy-preserving scheme maintains the confidentiality of personal preferences without severely compromising prediction accuracy.     

民航协同创新网络的集群特性分析

通过收集分析103例民航协同创新案例,以机构类型分布为研究视角,建立创新集群和协作网络的结构模型对比进行分析。采用复杂网络的研究方法建立民航协同创新协作网络,分析网络的整体特征,进一步运用聚类分析找出协同创新网络中协作关系较为紧密的集群类型,重点研究民航协同创新网络中的主要集群结构特征,找出网络中重点机构类型并研究其在网络中的作用,探讨与其他机构类型形成的集群现象,论证政产学研协作关系在民航协同创新发展中的重要作用。     

Personalization of tagging systems

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata.This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user’s tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user’s own preference and the opinion of others.     

Relevance-based language modelling for recommender systems

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.     

Unique-order interpolative coding for fast querying and space-efficient indexing in information retrieval systems

This paper presents a size reduction method for the inverted file, the most suitable indexing structure for an information retrieval system (IRS). We notice that in an inverted file the document identifiers for a given word are usually clustered. While this clustering property can be used in reducing the size of the inverted file, good compression as well as fast decompression must both be available. In this paper, we present a method that can facilitate coding and decoding processes for interpolative coding using recursion elimination and loop unwinding. We call this method the unique-order interpolative coding. It can calculate the lower and upper bounds of every document identifier for a binary code without using a recursive process, hence the decompression time can be greatly reduced. Moreover, it also can exploit document identifier clustering to compress the inverted file efficiently. Compared with the other well-known compression methods, our method provides fast decoding speed and excellent compression. This method can also be used to support a self-indexing strategy. Therefore our research work in this paper provides a feasible way to build a fast and space-economical IRS.     

Evaluating collaborative filtering recommendations inside large learning object repositories

Collaborative filtering (CF) algorithms are techniques used by recommender systems to predict the utility of items for users based on the similarity among their preferences and the preferences of other users. The enormous growth of learning objects on the internet and the availability of preferences of usage by the community of users in the existing learning object repositories (LORs) have opened the possibility of testing the efficiency of CF algorithms on recommending learning materials to the users of these communities. In this paper we evaluated recommendations of learning resources generated by different well known memory-based CF algorithms using two databases (with implicit and explicit ratings) gathered from the popular MERLOT repository. We have also contrasted the results of the generated recommendations with several existing endorsement mechanisms of the repository to explore possible relations among them. Finally, the recommendations generated by the different algorithms were compared in order to evaluate whether or not they were overlapping. The results found here can be used as a starting point for future studies that account for the specific context of learning object repositories and the different aspects of preference in learning resource selection.     

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques.     

Role-based results redistribution for collaborative information retrieval

We describe a new approach for algorithmic mediation of a collaborative search process. Unlike most approaches to collaborative IR, we are designing systems that mediate explicitly-defined synchronous collaboration among small groups of searchers with a shared information need. Such functionality is provided by first obtaining different rank-lists based on searchers’ queries, fusing these rank-lists, and then splitting the combined list to distribute documents among collaborators according to their roles. For the work reported here, we consider the case of two people collaborating on a search. We assign them roles of Gatherer and Surveyor: the Gatherer is tasked with exploring highly promising information on a given topic, and the Surveyor is tasked with digging further to explore more diverse information. We demonstrate how our technique provides the Gatherer with high-precision results, and the Surveyor with information that is high in entropy.     

CIRLab: A groupware framework for collaborative information retrieval research

In this paper we describe the design of a groupware framework, CIRLab, for experimenting with collaborative information retrieval (CIR) techniques in different search scenarios. This framework has been designed applying design patterns and an object-oriented middleware platform to maximize its reusability and adaptability in new contexts with a minimum of programming efforts. Our collaborative search application comprises three main modules: the Core, which supports various modern state-of-the-art CIR techniques that can be reused or extended in a distributed collaborative environment; the Facades Mediator, an event-driven notification service which allows easy integration between the Core and front-end applications; and finally, the Actions Tracker, which allows researchers to perform experiments on the different elements involved in the collaborative search sessions. The applying of this framework is illustrated through the analysis of the collaborative search-driven development case study.     

Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study

This paper proposes collaborative filtering as a means to predict semantic preferences by combining information on social ties with information on links between actors and semantics. First, the authors present an overview of the most relevant collaborative filtering approaches, showing how they work and how they differ. They then compare three different collaborative filtering algorithms using articles published by New York Times journalists from 2003 to 2005 to predict preferences, where preferences refer to journalists’ inclination to use certain words in their writing. Results show that while preference profile similarities in an actor’s neighbourhood are a good predictor of her semantic preferences, information on her social network adds little to prediction accuracy.     

Search on surfaces: Exploring the potential of interactive tabletops for collaborative search tasks

Collaborative information seeking often takes place in co-located settings; such opportunities may be planned (business colleagues meeting in a conference room or students working together in a library) or spontaneous (family members gathered in their living room or friends meeting at a café). Surface computing technologies (i.e., interactive tabletops) hold great potential for enhancing collaborative information seeking activities. Such devices provide engaging direct manipulation interactions, facilitate awareness of collaborators’ activities, and afford spatial organization of content. However, current tabletop technologies also present several challenges that creators of collaborative information seeking system must account for in their designs. In this article, we explore the design space for collaborative search systems on interactive tabletops, discussing the benefits and challenges of creating search applications for these devices. We discuss how features of our tabletop search prototypes TeamSearch, FourBySix Search, Cambiera, and WeSearch, illustrate different aspects of this design space.     

协同创新项目的动态利益分配方法

协同创新项目一般都是分阶段进行,且随着项目的进行,合作主体也在不断发生变化。基于此,以已经取得专利技术的高校寻求企业合作为起点,将之后的过程分为小试、中试和产业化,每阶段招募一家新企业为本阶段进行投资,新进入方会分享项目最终的收益,原有合作方的预期收益就会被稀释。以各合作成员预期收益最大化为目标,建立基于努力程度、贡献系数和成本系数的多目标多阶段利益分配模型,利用目标规划求解模型得出各阶段利益分配系数。通过算例分析,分配结果体现了风险补偿原则与投入补偿原则,验证了该分配方法的可操作性和实用性。     

Collaborative information seeking: The combined activity of information seeking and collaborative grounding

Since common ground is pivotal to collaboration, this paper proposes to define collaborative information seeking as the combined activity of information seeking and collaborative grounding. While information-seeking activities are necessary for collaborating actors to acquire new information, the activities involved in information seeking are often performed by varying subgroups of actors. Consequently, collaborative grounding is necessary to share information among collaborating actors and, thereby, establish and maintain the common ground necessary for their collaborative work. By focusing on the collaborative level, collaborative information seeking aims to avoid both individual reductionism and group reductionism, while at the same time recognizing that only some information and understanding need be shared.     

江西产业集群产学研协同创新平台的构建

本文总体围绕江西产业集群产学研协同创新平台的构建,分别讨论了产学研发展的现状、整体框架和功能设计。通过产学研协同创新平台的建设,构建以政府主导、行业引领、企业参与、科研机构和高等院校合作的协同创新体制、机制和运行模式。平台的实现有助于各类主体之间资源整合、优势互补,促进江西产业集群转型升级和区域经济社会的可持续发展。     

Connecting user and item perspectives in popularity debiasing for collaborative recommendation

Recommender systems learn from historical users’ feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality.     

Towards sustainable collaborative networks for smart cities co-governance

This paper addresses the concept of collaborative governance in the context of smart cities, with a focus on supporting and recommending performing organizational structures for sustainable collaborative networks (SCN). It highlights that governing a smart city is about promoting an effective environment of collaboration in the government and implying adaptive policy-making to construct new, internal and external human collaborations. Considering the smart governance as a collaborative network of government agencies and external stakeholders including citizens and a socio-technical system, we conduct in this paper an ethnographic mixed method by combining a qualitative method that studies actors’ collaboration and engagement in co-governance with a quantitative method that is based on graph theory to provide numerical analyses of organizational structures. While the qualitative method aims to discover organizational “smart factors” that affect the performance of SCN structures or configurations, the quantitative method aims to find “smart indicators” and metrics to evaluate these organizational factors. The result of this mixed method is an analytical recommender framework of the relevant SCN organizational structures in terms of robustness, flexibility and efficiency.     

A model for understanding collaborative information behavior in context: A study of two healthcare teams

Collaborative information behavior is an essential aspect of organizational work; however, we have very limited understanding of this behavior. Most models of information behavior focus on the individual seeker of information. In this paper, we report the results from two empirical studies that investigate aspects of collaborative information behavior in organizational settings. From these studies, we found that collaborative information behavior differs from individual information behavior with respect to how individuals interact with each other, the complexity of the information need, and the role of information technology. There are specific triggers for transitioning from individual to collaborative information behavior, including lack of domain expertise. The information retrieval technologies used affect collaborative information behavior by acting as important supporting mechanisms. From these results and prior work, we develop a model of collaborative information behavior along the axes of participant behavior, situational elements, and contextual triggers. We also present characteristics of collaborative information system including search, chat, and sharing. We discuss implications for the design of collaborative information retrieval systems and directions for future work.     

Personalized recommendation via user preference matching

Graph-based recommendation approaches use a graph model to represent the relationships between users and items, and exploit the graph structure to make recommendations. Recent graph-based recommendation approaches focused on capturing users’ pairwise preferences and utilized a graph model to exploit the relationships between different entities in the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits both explicit and implicit feedback of users. The algorithm utilizes a user-preference-item tripartite graph model and modified resource allocation process to match the target user with users who share similar preferences, and make personalized recommendations. The principle of the additional preference layer is to capture users’ pairwise preferences, provide detailed information of users for further recommendations. Empirical analysis of four benchmark datasets demonstrated that our proposed algorithm performs better in most situations than other graph-based and ranking-oriented benchmark algorithms.