Context relevance assessment and exploitation in mobile recommender systems

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.     

Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities

Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.     

Blockchain-based recommender systems: Applications,challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research.     

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.     

Context-aware mobile communication in hospitals

A, collaborative handheld system extends the instant messaging paradigm by adding context-awareness to support the intensive and distributed nature of information management within a hospital setting.     

Extraction of road surface from mobile LiDAR data of complex road environment

Three-dimensional (3D) data of roadways are frequently used for extraction of detailed roadway information which is essential for several planning and engineering applications. Recent past has seen rapid growth in utilization of mobile LiDAR system (MLS) to acquire volumetric 3D data of roadway for this purpose. MLS data are capable of capturing highly detailed road information, which is useful for road maintenance and road safety operations. The existing literature shows that road environment complexity, unevenness, and absence of raised curb limit the extraction of road information from MLS data. It must be noted that a large number of roads, especially in developing world, are characterized by these complexities and thus raise the need for a technique which can work in these road environments. Considering the above, this paper proposes a method to extract road information, where road boundary is not geometrically well-defined. The proposed method is constructed using unstructured MLS data as input and does not require any other additional data. The method is divided into three major steps, that is, MLS data structuring and ground filtering, road surface point extraction, and road boundary refinement. The first step filters ground points from input MLS data, while the second step identifies road surface points from among the ground points. The second step is designed using specific characteristics of a road, that is, topology, surface roughness, and variation of point density. Third step refines road boundary. Three test sites, quite complex with heterogeneous characteristics, were used for demonstration of the proposed method. Road surfaces of these three roadways were accurately extracted without being affected by on-road objects and absence of raised curb. Average accuracy measures like completeness, correctness, and quality were found to be 93.8%, 98.3%, and 92.3%, respectively, in three test sites. Further, road boundaries of extracted road surfaces of these three test sites were refined at average completeness, correctness, and quality of 95.6%, 97.9%, and 93.7%, respectively. The proposed method has shown satisfactory performance for complex roadways having road section with and without raised curb, and has potential to be employed for such road environments, which are not uncommon. Proposed method was implemented on GPU-based parallel computing framework, which significantly saved the run time in processing of MLS data of three test sites.     

A literature review and classification of recommender systems research

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10 years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.     

Towards psychology-aware preference construction in recommender systems: Overview and research issues

Journal of Intelligent Information Systems - User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as...     

VISCORS: a visual-content recommender for the mobile Web

Current search methods for mobile-Web content can be frustrating to use. To shorten searches for cell phone wallpaper images, VISCORS combines collaborative filtering with content-based image retrieval. An increasing selection of content is becoming available in the mobile-Web environment, where users navigate the Web using wireless devices such as cell phones and PDAs. The fast growth and excellent prospects of the mobile-Web content market have attracted many content providers.     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

Timeliness in recommender systems

Due to the high efficiency in finding the most relevant online products for users from the information ocean, recommender systems have now been applied to many commercial web sites. Meanwhile, many recommendation algorithms have been developed to improve the recommendation accuracy and diversity. However, whether the recommended items are timely or not in these algorithms has not yet been well understood. To investigate this problem, we consider a temporal data division which divides the links to probe set and training set strictly according to the time stamp on links. We find that the recommendation accuracy of many algorithms are much lower in temporal data division than in the random data division.With a timeliness metric, we find that the low accuracy is caused by the tendency of these algorithms to recommend out-of-date items, which cannot be detected with the random data division. To solve this problem, we improve the considered recommendation algorithms with a timeliness factor. The resulting algorithms can strongly suppress the probability of recommending obsolete items. Meanwhile, the recommendation accuracy is substantially enhanced.     

移动设备的个性化推荐在上下文感知应用

个性化推荐对上下文感知系统具有广泛而重要应用,现在大多数个性化推荐系统很少考虑用户的认知风格.文中比较了场独立用户和场依赖用户在上下文感知环境中所存在的差异,提出一种基于用户认知风格的个性化推荐系统(CSBRS).通过CSBRS的实现例子,表明基于用户认知风格的个性化推荐有助于提高个性化推荐的质量.     

Context-aware broadcasting approaches in mobile ad hoc networks

The aim of this paper is to compare different context-aware broadcasting approaches in mobile ad hoc networks (MANETs) and to evaluate their respective performances. Message broadcasting is one of the core challenges brought up by distributed systems and has therefore largely been studied in the context of traditional network structures, such as the Internet. With the emergence of MANETs, new broadcasting algorithms especially geared at these networks have been introduced. The goal of these broadcasting algorithms is to ensure that a maximum number of nodes deliver the broadcasted message (reliability), while ensuring that the minimum number of nodes retransmit the broadcasted message (efficiency), in order to save their resources, such as bandwidth or battery. In recent years, as more and more mobile devices have become context-aware, several broadcasting algorithms have been introduced that take advantage of contextual information in order to improve their performance. We distinguish four approaches with respect to context: (1) context-oblivious approaches, (2) network traffic-aware approaches, (3) power-aware approaches, and (4) location-aware approaches. This paper precisely aims at presenting these four different broadcasting approaches and at measuring the performance of algorithms built upon them.     

Privacy risks in recommender systems

Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries     

Goal-based structuring in recommender systems

Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using recommendations) taking into account the users' immediate interests: a goal-based structuring method. Goal-based structuring is based on the fact that people experience certain gratifications from using information, which should match with their goals. An experiment using an electronic TV guide shows that structuring information using a goal-based structure makes it easier for users to find interesting information, especially if the goals are used explicitly; this is independent of whether recommendations are used or not. It also shows that goal-based structuring has more influence on how easy it is for users to find interesting information than recommendations.     

The future of systems research

After 20 years in academia and the Silicon Valley, the new Provost of Stanford University calls for a shift in focus for systems research. Performance-long the centerpiece-needs to share the spotlight with availability, maintainability, and other qualities. Although performance increases over the past 15 years have been truly amazing, it will be hard to continue these trends by sticking to the basically evolutionary path that the research community is currently on. The author advocates a more evolutionary approach to systems problems and thinks the approach needs to be more integrated. Researchers need to think about hardware and software as a continuum, not as separate parts. He sees society on the threshold of a “post PC” era, in which computing is ubiquitous, and everyone will use information services and everyday utilities. When everyone starts using these systems, guess what? They expect them to work and to be easy to use. So this era will drive system design toward greater availability, maintainability, and scalability, which will require a real refocusing of current research directions     

公共环境下的混合型音乐推荐系统的关键技术研究

随着计算机网络和多媒体技术的迅速普及,数字音乐消费已经成为人们日常生活中的常见活动,音乐推荐系统也因此成为了推荐系统和电子商务领域的一大研究热点。在对现有音乐推荐系统调研的基础上,重点研究公共环境下的混合型音乐推荐系统的设计和实现;将音乐特征和语境信息相结合,提出了一种新颖的混合型音乐推荐算法。为保证实际应用环境中音乐消费行为的灵活性,系统实现了投票和DJ两种推荐模式。该系统在实验室及实地测试中均取得了较高的用户满意度。     

Context-aware mobile service for routing the fastest subway path

The rapid growth of the IT industry during the last few decades has increased demands on mobile devices such as PDAs, cellular phones, and GPS navigation systems. With emerging concepts of context-aware computing, the mobile devices can provide mobile users with timely information by using not only common knowledge but also environmental context such as current time and location. Lately, the context-aware applications have been actively investigated and have been contributed to numerous application areas such as real-time electronic catalogues and navigation systems for tourists. In this paper, we propose a new context-aware application for finding the fastest subway route. We have developed the proposed application as an implemented system named Optimize Your Time System (OYT System, for short). A terminal device of the OYT System is equipped with a GPS receiver and the system’s server contains a timetable of all trains in a target subway system. On perceiving users’ context such as current time and location automatically from GPS, the OYT System can display the optimal route which takes the shortest time for the user to reach the specified destination. In this paper, we present details of the OYT System and some experimental examples.