Provider fairness across continents in collaborative recommender systems

When a recommender system suggests items to the end-users, it gives a certain exposure to the providers behind the recommended items. Indeed, the system offers a possibility to the items of those providers of being reached and consumed by the end-users. Hence, according to how recommendation lists are shaped, the experience of under-recommended providers in online platforms can be affected. To study this phenomenon, we focus on movie and book recommendation and enrich two datasets with the continent of production of an item. We use this data to characterize imbalances in the distribution of the user–item observations and regarding where items are produced (geographic imbalance). To assess if recommender systems generate a disparate impact and (dis)advantage a group, we divide items into groups, based on their continent of production, and characterize how represented is each group in the data. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. We observe disparities that favor the most represented groups. We overcome these phenomena by introducing equity with a re-ranking approach that regulates the share of recommendations given to the items produced in a continent (visibility) and the positions in which items are ranked in the recommendation list (exposure), with a negligible loss in effectiveness, thus controlling fairness of providers coming from different continents. A comparison with the state of the art shows that our approach can provide more equity for providers, both in terms of visibility and of exposure.     

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.     

Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems

Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user–item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.     

A recommender mechanism for social knowledge navigation in an online encyclopedia

In today’s world, knowledge is important for constructing core competitive advantages for individuals and organizations. Recently, Web 2.0 applications and social media have provided a convenient medium for people to share knowledge over the Internet. However, the huge amount of created knowledge can also leads to the problem of information overload. This research proposes a social knowledge navigation mechanism that utilizes the techniques of relevant knowledge network construction, knowledge importance analysis, and knowledge concept ontology construction to generate a visualized recommendation of a knowledge map of sub-concept and knowledge of an article reading sequence for supporting learning activities related to a free online encyclopedia. The results of experiments conducted on Wikipedia show that the proposed mechanism can effectively recommend useful articles and improve a knowledge seeker’s learning effectiveness.     

A social recommender mechanism for improving knowledge sharing in online forums

Nowadays, online forums have become a useful tool for knowledge management in Web-based technology. This study proposes a social recommender system which generates discussion thread and expert recommendations based on semantic similarity, profession and reliability, social intimacy and popularity, and social network-based Markov Chain (SNMC) models for knowledge sharing in online forum communities. The advantage of the proposed mechanism is its relatively comprehensive consideration of the aspects of knowledge sharing. Accordingly, results of our experiments show that with the support of the proposed recommendation mechanism, requesters in forums can easily find similar discussion threads to avoid spamming the same discussion. In addition, if the requesters cannot find qualified discussion threads, this mechanism provides a relatively efficient and active way to find the appropriate experts.     

Practical perspectives of consumer fairness in recommendation

In recent years, there has been an increasing number of mitigation procedures against consumer unfairness in personalized rankings. However, the experimental protocols adopted so far for evaluating a mitigation procedure were often fundamentally different (e.g., with respect to the fairness definitions, data sets, data splits, and evaluation metrics) and limited to a narrow set of perspectives (e.g., focusing on a single demographic attribute and/or not reporting any analysis on efficiency). This situation makes it challenging for scientists to consciously decide which mitigation procedure better suits their practical setting. In this paper, we investigated the properties a given mitigation procedure against consumer unfairness should be evaluated on, to provide a more holistic view on its effectiveness. We first identified eight technical properties and evaluated the extent to which existing mitigation procedures against consumer unfairness met these properties, qualitatively and quantitatively (when possible), on two public data sets. Then, we outlined the main trends and open issues emerged from our multi-dimensional analysis and provided key practical recommendations for future research. The source code accompanying this paper is available at https://github.com/jackmedda/Perspective-C-Fairness-RecSys.     

Incorporating group recommendations to recommender systems: Alternatives and performance

In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group’s users’ data can be aggregated to the data of the group of users: similarity metric, establishing the neighborhood, prediction phase, determination of recommended items. In this paper we perform aggregation experiments in each of the four stages and two fundamental conclusions are reached: (1) the system accuracy does not vary significantly according to the stage where the aggregation is performed, (2) the system performance improves notably when the aggregation is performed in an earlier stage of the collaborative filtering process. This paper provides a group recommendation similarity metric and demonstrates the convenience of tackling the aggregation of the group’s users in the actual similarity metric of the collaborative filtering process.     

Social network data to alleviate cold-start in recommender system: A systematic review

Recommender Systems are currently highly relevant for helping users deal with the information overload they suffer from the large volume of data on the web, and automatically suggest the most appropriate items that meet users needs. However, in cases in which a user is new to Recommender System, the system cannot recommend items that are relevant to her/him because of lack of previous information about the user and/or the user-item rating history that helps to determine the users preferences. This problem is known as cold-start, which remains open because it does not have a final solution. Social networks have been employed as a good source of information to determine users preferences to mitigate the cold-start problem. This paper presents the results of a Systematic Literature Review on Collaborative Filtering-based Recommender System that uses social network data to mitigate the cold-start problem. This Systematic Literature Review compiled the papers published between 2011–2017, to select the most recent studies in the area. Each selected paper was evaluated and classified according to the depth which social networks used to mitigate the cold-start problem. The final results show that there are several publications that use the information of the social networks within the Recommender System; however, few research papers currently use this data to mitigate the cold-start problem.     

A general end-to-end diagnosis framework for manufacturing systems

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.     

A probabilistic music recommender considering user opinions and audio features

A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains.     

Permissible control of general constrained mechanical systems

This paper develops a unified approach for modeling and controlling mechanical systems that are constrained with general holonomic and nonholonomic constraints. The approach conceptually distinguishes and separates constraints that are imposed on the mechanical system for developing its physical structure between constraints that may be used for control purposes. This gives way to a general class of nonlinear control systems for constrained mechanical systems in which the control inputs are viewed as the permissible control forces. In light of this view, a new and simple technique for designing nonlinear state feedback controllers for constrained mechanical systems is presented. The general applicability of the approach is demonstrated by considering the nonlinear control of an underactuated system.     

Continuance intention in gamified mobile applications: A study of behavioral inhibition and activation systems

This study seeks to investigate the effects of Behavioral Activation System (BAS), known as approach motivation, and Behavioral Inhibition System (BIS), known as avoidance motivation, that are expected to influence individuals’ post-adoption behaviors in gamified mobile applications. A survey-based research methodology was used, and the impacts of BAS and BIS on Information Systems Continuance are examined. The results show that reward responsiveness has the most significant effect on user satisfaction and continuance intention. Fun-seeking plays an essential role in continuance intention; however, it does not significantly affect satisfaction, as drive does not affect either satisfaction or continuance intention. BIS has a significant and negative effect on individual satisfaction, but no effects were found related to continuance intention. The findings of this study improve the understanding of the differences between these motivations related to Information Systems Continuance. Significant practical implications that gamified mobile application developers can adopt are proposed.     

An investigation of the strategic information systems planning success in Chinese publicly traded firms

Based on a survey of Chinese publicly traded firms, we report on the status of the strategic information systems planning success in China. Through this analysis of the survey data, we found that Chinese managers are not using IS as a competitive weapon though they have already realized some aspects of strategic information system planning. We also examined the differences between Eastern and Western cultural and political context factors to explain this phenomenon.     

Projects as communicating systems: Creating a culture of innovation and performance

Although often downplayed and instrumental, there is evidence that communication in projects is essential in achieving value creation. Our main interest in this paper is on temporary continuity, a situation where the temporary becomes a permanent condition in social systems. The question that we have address is: What characterizes project communication in a situation with temporary continuity?We argue for the need to transform communication processes into communication capabilities. In a situation with temporary continuity, there is a need to connect to a large number of value-creating processes, and communicating capabilities need to be a part of a communication system, where the aim is to bind together value-creating processes and communication capabilities. We construct a viable system consisting of five sub-systems. To become a viable system, projects in the form of temporary continuity, must handle the potential conflict between a culture of performance and a culture of innovation. This involves developing social mechanisms for coordination and interaction, with a focus on developing communication capabilities, in parallel with focusing on all of the five value-creation processes.     

Dynamic and emergent information systems strategy formulation and implementation

Early attempts to formulate information systems (IS) strategies concentrated on the analytical task of deriving IS strategies from business plans. The limitations of the static plans that often resulted from these formal studies were, however, soon discovered. The critics suggested informal and incremental planning to ensure flexibility, creativity and strategic thinking to comprise emergent strategies as well as planned strategies.In previous IS planning research, there appears to be a contradiction between the published planning methods and the generally held views about effective implementation of IS planning process. The explicit methods described in IS literature predominantly assume a comprehensive IS planning process. Despite the fact that many researchers consider incremental approaches to be more effective, methods that can be used to facilitate incremental IS planning are few, not detailed enough and not comprehensive.The four cycles method introduced in this paper attempts to combine the strengths of both the comprehensive and incremental planning to be able to recognise emerging trends and to make an e-business strategy. The method provides a basic schedule for organising planning activities. IS planning is seen as a continuous process that is periodically adjusted to the expectations of the participating managers. Practising managers can use the method to facilitate implementation of an incremental and continuous IS planning process. For e-business strategy research, the paper provides a theoretically based method that can be tested in future action research projects.The first results of conducted action research show that the method should not be used as a checklist but as a choice list. Each period had a constant focus on external developments and the fit with internal possibilities. The method provided a flexible and dynamic basis for actions. The emergent nature of the changes and the difficulty of formalising creativity and innovation placed restrictions on the planning process. We learned that a thematic approach where each cycle is given a creative subject helped to “open up” the users in the organisation. Future research should focus on the inter-organisational nature of e-business strategy. If it is difficult to get top management participation, it will be even more difficult with more organisations involved.